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Top 30 Machine Learning Skills required to get a Machine Learning Job

Machine learning has been making a silent revolution in our lives since the past decade. From capturing selfies with a blurry background and focused face capture to getting our queries answered by virtual assistants such as Siri and Alexa, we are increasingly depending on products and applications that implement machine learning at their core.In more basic terms, machine learning is one of the steps involved in artificial intelligence. Machines learn through machine learning. How exactly? Just like how humans learn – through training, experience, and feedback.Once machines learn through machine learning, they implement the knowledge so acquired for many purposes including, but not limited to, sorting, diagnosis, robotics, analysis and predictions in many fields.It is these implementations and applications that have made machine learning an in-demand skill in the field of programming and technology.Look at the stats that show a positive trend for machine learning projects and careers.Gartner’s report on artificial intelligence showed that as many as 2.3 million jobs in machine learning would be available across the globe by 2020.Another study from Indeed, the online job portal giant, revealed that machine learning engineers, data scientists and software engineers with these skills are topping the list of most in-demand professionals.High profile companies such as Univa, Microsoft, Apple, Google, and Amazon have invested millions of dollars on machine learning research and designing and are developing their future projects on it.With so much happening around machine learning, it is no surprise that any enthusiast who is keen on shaping their career in software programming and technology would prefer machine learning as a foundation to their career. This post is specifically aimed at guiding such enthusiasts and gives comprehensive information on skills that are needed to become a machine learning engineer, who is ready to dive into the real-time challenges.Machine Learning SkillsOrganizations are showing massive interest in using machine learning in their products, which would in turn bring plenty of opportunities for machine learning enthusiasts.When you ask machine learning engineers the question – “What do you do as a machine learning engineer?”, chances are high that individual answers would differ from one professional to another. This may sound a little puzzling, but yes, this is true!Hence, a beginner to machine learning needs to have a clear understanding that there are different roles that they can perform with machine learning skills. And accordingly the skill set that they should possess, would differ. This section will give clarity on machine learning skills that are needed to perform various machine learning roles.Broadly, three main roles come into the picture when you talk about machine learning skills:Data EngineerMachine Learning EngineerMachine Learning ScientistOne must understand that data science, machine learning and artificial intelligence are interlinked. The following quote explains this better:Data science produces insights. Machine learning produces predictions. Artificial intelligence produces actions.A machine learning engineer is someone who deals with huge volumes of data to train a machine and impart it with knowledge that it uses to perform a specified task. However, in practice, there may be a little more to add to this:Machine Learning RoleSkills RequiredRoles and ResponsibilitiesData EngineerPython, R, and DatabasesParallel and distributed Knowledge on quality and reliabilityVirtual machines and cloud environmentMapReduce and HadoopCleaning, manipulating and extracting the required data   Developing code for data analysis and manipulationPlays a major role in statistical analysis of dataMachine Learning EngineerConcepts of computer science and software engineeringData analysis and feature engineeringMetrics involved in MLML algorithm selection, and cross validationMath and StatisticsAnalyses and checks the suitability of an algorithm if it caters the needs of the current taskPlays main role in deciding and selecting machine learning libraries for given task.Machine Learning ScientistExpert knowledge in:Robotics and Machine LearningCognitive ScienceEngineeringMathematics and mathematical modelsDesigns new models and algorithms of machine learningResearches intensively on machine learning and publishes their research papers.Thus, gaining machine learning skills should be a task associated with clarity on the job role and of course the passion to learn them. As it is widely known, becoming a machine learning engineer is not a straightforward task like becoming a web developer or a tester.Irrespective of the role, a learner is expected to have solid knowledge on data science. Besides, many other subjects are intricately intertwined in learning machine learning and for a learner it requires a lot of patience and zeal to learn skills and build them up as they move ahead in their career.The following diagram shows the machine learning skills that are in demand year after year:AI - Artificial IntelligenceTensorFlowApache KafkaData ScienceAWS - Amazon Web Services                                                                                                                                                                                                                                                                                                                                Image SourceIn the coming sections, we would be discussing each of these skills in detail and how proficient you are expected to be in them.Technical skills required to become ML EngineerBecoming a machine learning engineer means preparing oneself to handle interesting and challenging tasks that would change the way humanity is experiencing things right now. It demands both technical and non-technical expertise. Firstly, let’s talk about the technical skills needed for a machine learning engineer. Here is a list of technical skills a machine learning engineer is expected to possess:Applied MathematicsNeural Network ArchitecturesPhysicsData Modeling and EvaluationAdvances Signal Processing TechniquesNatural Language ProcessingAudio and video ProcessingReinforcement LearningLet us delve into each skill in detail now:1.Applied MathematicsMathematics plays an important role in machine learning, and hence it is the first one on the list. If you wish to see yourself as a proven machine learning engineer, you ought to love math and be an expert in the following specializations of math.But first let us understand why a machine learning engineer would need math at all? There are many scenarios where a machine learning engineer should depend on math. For example:Choosing the right algorithm that suits the final needsUnderstanding and working with parameters and their settings.Deciding on validation strategiesApproximating the confidence intervals.How much proficiency in Math does a machine learning engineer need to have?It depends on the level at which a machine learning engineer works. The following diagram gives an idea about how important various concepts of math are for a machine learning enthusiast.A) Linear algebra: 15%B) Probability Theory and Statistics: 25%C) Multivariate Calculus: 15%D) Algorithms and Optimization: 15%F) Other concepts: 10%Data SourceA) Linear AlgebraThis concept plays a main role in machine learning. One has to be skilled in the following sub-topics of linear algebra:Principal Component Analysis (PCA), Singular Value Decomposition (SVD)Eigen decomposition of a matrixLU DecompositionQR Decomposition/FactorizationSymmetric MatricesOrthogonalization & OrthonormalizationMatrix OperationsProjectionsEigenvalues & EigenvectorsVector Spaces and NormsB) Probability Theory and StatisticsThe core aim of machine learning is to reduce the probability of error in the final output and decision making of the machine. Thus, it is no wonder that probability and statistics play a major role.The following topics are important in these subjects:CombinatoricsProbability Rules & AxiomsBayes’ TheoremRandom VariablesVariance and ExpectationConditional and Joint DistributionsStandard Distributions (Bernoulli, Binomial, Multinomial, Uniform and Gaussian)Moment Generating Functions, Maximum Likelihood Estimation (MLE)Prior and PosteriorMaximum a Posteriori Estimation (MAP)Sampling Methods.C) CalculusIn calculus, the following concepts have notable importance in machine learning:Integral CalculusPartial Derivatives,Vector-Values FunctionsDirectional GradientHessian, Jacobian, Laplacian and Lagrangian Distributions.D) Algorithms and OptimizationThe scalability and the efficiency of computation of a machine learning algorithm depends on the chosen algorithm and optimization technique adopted. The following areas are important from this perspective:Data structures (Binary Trees, Hashing, Heap, Stack etc)Dynamic ProgrammingRandomized & Sublinear AlgorithmGraphsGradient/Stochastic DescentsPrimal-Dual methodsE) Other ConceptsBesides, the ones mentioned above, other concepts of mathematics are also important for a learner of machine learning. They are given below:Real and Complex Analysis (Sets and Sequences, Topology, Metric Spaces, Single-Valued and Continuous Functions Limits, Cauchy Kernel, Fourier Transforms)Information Theory (Entropy, Information Gain)Function Spaces and Manifolds2.Neural Network ArchitecturesNeural networks are the predefined set of algorithms for implementing machine learning tasks. They offer a class of models and play a key role in machine learning.The following are the key reasons why a machine learning enthusiast needs to be skilled in neural networks:Neural networks let one understand how the human brain works and help to model and simulate an artificial one.Neural networks give a deeper insight of parallel computations and sequential computationsThe following are the areas of neural networks that are important for machine learning:Perceptrons Convolutional Neural Networks Recurrent Neural NetworkLong/Short Term Memory Network (LSTM)Hopfield Networks Boltzmann Machine NetworkDeep Belief NetworkDeep Auto-encoders3.PhysicsHaving an idea of physics definitely helps a machine learning engineer. It makes a difference in designing complex systems and is a skill that is a definite bonus for a machine learning enthusiast.4.Data Modeling and EvaluationA machine learning has to work with huge amounts of data and leverage them into predictive analytics. Data modeling and evaluation is important in working with such bulky volumes of data and estimating how good the final model is.For this purpose, the following concepts are worth learnable for a machine learning engineer:Classification AccuracyLogarithmic LossConfusion MatrixArea under CurveF1 ScoreMean Absolute ErrorMean Squared Error5.Advanced Signal Processing TechniquesThe crux of signal processing is to minimize noise and extract the best features of a given signal.For this purpose, it uses certain concepts such as:convex/greedy optimization theory and algorithmsspectral time-frequency analysis of signalsAlgorithms such as wavelets, shearlets, curvelets, contourlets, bandlets, etc.All these concepts find their application in machine learning as well.6. Natural language processingThe importance of natural language processing in artificial intelligence and machine learning is not to be forgotten. Various libraries and techniques of natural language processing used in machine learning are listed here:Gensim and NLTKWord2vecSentiment analysisSummarization7. Audio and Video ProcessingThis differs from natural language processing in the sense that we can apply audio and video processing on audio signals only. For achieving this, the following concepts are essential for a machine learning engineer:Fourier transformsMusic theoryTensorFlow8. Reinforcement LearningThough reinforcement learning plays a major role in learning and understanding deep learning and artificial intelligence, it is good for a beginner of machine learning to know the basic concepts of reinforcement learning.Programming skills required to become ML EngineerMachine learning, ultimately, is coding and feeding the code to the machines and getting them to do the tasks we intend them to do. As such, a machine learning engineer should have hands-on expertise in software programming and related concepts. Here is a list of programming skills a machine learning engineer is expected to have knowledge on:Computer Science Fundamentals and ProgrammingSoftware Engineering and System DesignMachine Learning Algorithms and LibrariesDistributed computingUnixLet us look into each of these programming skills in detail now:1.Computer Science Fundamentals and ProgrammingIt is important that a machine learning engineer apply the concepts of computer science and programming correctly as the situation demands. The following concepts play an important role in machine learning and are a must on the list of the skillsets a machine learning engineer needs to have:Data structures (stacks, queues, multi-dimensional arrays, trees, graphs)Algorithms (searching, sorting, optimization, dynamic programming)Computability and complexity (P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc.)Computer architecture (memory, cache, bandwidth, deadlocks, distributed processing, etc.)2.Software Engineering and System DesignWhatever a machine learning engineer does, ultimately it is a piece of software code – a beautiful conglomerate of many essential concepts and the one that is entirely different from coding in other software languages.Hence, it is quintessential that a machine learning engineer have solid knowledge of the following areas of software programming and system design:Scaling algorithms with the size of dataBasic best practices of software coding and design, such as requirement analysis, version control, and testing.Communicating with different modules and components of work using library calls, REST APIs and querying through databases.Best measures to avoid bottlenecks and designing the final product such that it is user-friendly.3. Machine Learning Algorithms and LibrariesA machine learning engineer may need to work with multiple packages, libraries, algorithms as a part of day-to-day tasks. It is important that a machine learning engineer is well-versed with the following aspects of machine learning algorithms and libraries:A thorough idea of various learning procedures including linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods.Sound knowledge in packages and APIs such as scikit-learn, Theano, Spark MLlib, H2O, TensorFlow, etc.Expertise in models such as decision trees, nearest neighbor, neural net, support vector machine and a knack to deciding which one fits the best.Deciding and choosing hyperparameters that affect learning model and the outcome.Comfortable to work with concepts such as gradient descent, convex optimization, quadratic programming, partial differential equations.Select an algorithm which yields the best performance from random forests, support vector machines (SVMs), and Naive Bayes Classifiers, etc.4   Distributed Computing Working as a machine learning engineer means working with huge sets of data, not just focused on one isolated system, but spread among a cluster of systems. For this purpose, it is important that a machine learning engineer knows the concepts of distributed computing.5. UnixMost clusters and servers that machine learning engineers need to work are variants of Linux(Unix). Though randomly they work on Windows and Mac, more than half of the time, they need to work on Unix systems only. Hence having sound knowledge on Unix and Linux is a key skill to become a machine learning engineer.Programming Languages for Machine LearningMachine learning engineers need to code to train machines. Several programming languages can be used to do this. The list of programming languages that a machine learning expert should essentially know are as under:C, C++ and JavaSpark and HadoopR ProgrammingApache KafkaPythonWeka PlatformMATLAB/OctaveIn this section, let us know in detail why each of these programming languages is important for a machine learning engineer:1.C, C++ and JavaThese languages give essentials of programming and teach many concepts in a simple manner that form a foundation stone for working on complex programming patterns of machine learning. Knowledge of C++ helps to improve the speed of the program, while Java is needed to work with Hadoop and Hive, and other tools that are essential for a machine learning engineer.2.Spark and HadoopHadoop skills are needed for working in a distributed computing environment. Spark, a recent variant of Hadoop is gaining popularity among the machine learning tribe. It is a framework to implement machine learning on a large scale.3.R ProgrammingR is a programming language built by statisticians specifically to work with programming that involves statistics. Many mathematical computations of machine learning are based on statistics; hence it is no wonder that a machine learning engineer needs to have sound knowledge in R programming.4.Apache KafkaApache Kafka concepts such as Kafka Streams and KSQL play a major role in pre-processing of data in machine learning. Also, a sound knowledge of Apache Kafka lets a machine learning engineer to design solutions that are both multi-cloud based or hybrid cloud-based.  Other concepts such as business information such as latency and model accuracy are also from Kafka and find use in Machine learning.5.PythonOf late, Python has become the unanimous programming language for machine learning. In fact, experts quote that humans communicate with machines through Python language.Why Python is preferred for Machine Learning?Python Programming Language has several key features and benefits that make it the monarch of programming languages for machine learning:It is an all-in-one purpose programming language that can do a lot more than dealing with statistics.It is beginner friendly and easy to learn.It boasts of rich libraries and APIs that solve various needs of machine learning pretty easily.Its productivity is higher than its other counterparts.It offers ease of integration and gets the workflow smoothly from the designing stage to the production stage.Python EcoSystemThere are various components of Python that make it preferred language for machine learning. Such components are discussed below:Jupyter NotebookNumpyPandasScikit-LearnTensorFlow1.Jupyter NotebookJupyter offers excellent computational environment for Python based data science applications. With the help of Jupyter notebook, a machine learning engineer can illustrate the flow of the process step-by-step very clearly.2.NumPyNumPy or Numerical Python is one of the components of Python that allows the following operations of machine learning in a smooth way:Fourier transformationLinear algebraic operationsLogical and numerical operations on arrays.Of late, NumPy is gaining attention because it makes an excellent substitute to MATLAB, as it coordinates with Matplotlib and SciPy very smoothly.3.PandasPandas is a Python library that offers various features for loading, manipulating, analysing, modeling and preparing data. It is entirely dedicated for data analysis and manipulation.4.Scikit-learnBuilt on NumPy, SciPy, and Matplotlib, it is an open-source library of Python. It offers excellent features and functionalities for major aspects of machine learning such as clustering, dimensionality reduction, model reduction, regression and classification.5.TensorFlowTensorFlow is another framework of Python. It finds its usage in deep learning and having a knowledge of its libraries such as Keras, helps a machine learning engineer to move ahead confidently in their career.6.Weka PlatformIt is widely known that machine learning is a non-linear process that involves many iterations. Weka or Waikato Environment for Knowledge Analysis is a recent platform that is designed specifically designed for applied machine learning. This tool is also slowing gaining its popularity and thus is a must-include on the list of skills for a machine learning engineer.7.MATLAB/OctaveThis is a basic programming language that was used for simulation of various engineering models. Though not popularly used in machine learning, having sound knowledge in MATLAB lets one learns the other mentioned libraries of Python easily.Soft skills or behavioural skills required to become ML engineerTechnical skills are relevant only when they are paired with good soft skills. And the machine learning profession is no exception to this rule. Here is a list of soft skills that a machine learning engineer should have:Domain knowledgeCommunication SkillsProblem-solving skillsRapid prototypingTime managementLove towards constant learningLet us move ahead and discuss how each of these skills make a difference to a machine learning engineer.1.Domain knowledgeMachine learning is such a subject that needs the best of its application in real-time. Choosing the best algorithm while solving a machine learning problem in your academia is far different from what you do in practice. Various aspects of business come into picture when you are a real-time machine learning engineer. Hence, a solid understanding of the business and domain of machine learning is of utmost importance to succeed as a good machine learning engineer.2.Communication SkillsAs a machine learning engineer, you need to communicate with offshore teams, clients and other business teams. Excellent communication skills are a must to boost your reputation and confidence and to bring up your work in front of peers.3.Problem-solving skillsMachine learning is all about solving real time challenges. One must have good problem-solving skills and be able to weigh the pros and cons of the given problem and apply the best possible methods to solve it.4.Rapid PrototypingChoosing the correct learning method or the algorithm are signs of a machine learning engineer’s good prototyping skills. These skills would be a great saviour in real time as they would show a huge impact on budget and time taken for successfully completing a machine learning project.5.Time managementTraining a machine is not a cake-walk. It takes huge time and patience to train a machine. But it’s not always that machine learning engineers are allotted ample time for completing tasks. Hence, time management is an essential skill a machine learning professional should have to effectively deal with bottlenecks and deadlines.6.Love towards constant learningSince its inception, machine learning has witnessed massive change – both in the way it is implemented and in its final form. As we have seen in the previous section, technical and programming skills that are needed for machine learning are constantly evolving. Hence, to prove oneself a successful machine learning expert, it is very crucial that they have a zeal to update themselves – constantly!ConclusionThe skills that one requires to begin their journey in machine learning are exactly what we have discussed in this post. The future for machine learning is undoubtedly bright with companies ready to offer millions of dollars as remuneration, irrespective of the country and the location.Machine learning and deep learning will create a new set of hot jobs in the next five years. – Dave WatersAll it takes to have an amazing career in machine learning is a strong will to hone one’s skills and gain a solid knowledge of them. All the best for an amazing career in machine learning!
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Top 30 Machine Learning Skills required to get a Machine Learning Job

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Top 30 Machine Learning Skills required to get a Machine Learning Job

Machine learning has been making a silent revolution in our lives since the past decade. From capturing selfies with a blurry background and focused face capture to getting our queries answered by virtual assistants such as Siri and Alexa, we are increasingly depending on products and applications that implement machine learning at their core.

In more basic terms, machine learning is one of the steps involved in artificial intelligence. Machines learn through machine learning. How exactly? Just like how humans learn – through training, experience, and feedback.

Once machines learn through machine learning, they implement the knowledge so acquired for many purposes including, but not limited to, sorting, diagnosis, robotics, analysis and predictions in many fields.

It is these implementations and applications that have made machine learning an in-demand skill in the field of programming and technology.

Look at the stats that show a positive trend for machine learning projects and careers.

  1. Gartner’s report on artificial intelligence showed that as many as 2.3 million jobs in machine learning would be available across the globe by 2020.
  2. Another study from Indeed, the online job portal giant, revealed that machine learning engineers, data scientists and software engineers with these skills are topping the list of most in-demand professionals.
  3. High profile companies such as Univa, Microsoft, Apple, Google, and Amazon have invested millions of dollars on machine learning research and designing and are developing their future projects on it.

With so much happening around machine learning, it is no surprise that any enthusiast who is keen on shaping their career in software programming and technology would prefer machine learning as a foundation to their career. This post is specifically aimed at guiding such enthusiasts and gives comprehensive information on skills that are needed to become a machine learning engineer, who is ready to dive into the real-time challenges.

Machine Learning Skills

Organizations are showing massive interest in using machine learning in their products, which would in turn bring plenty of opportunities for machine learning enthusiasts.

When you ask machine learning engineers the question – “What do you do as a machine learning engineer?”, chances are high that individual answers would differ from one professional to another. This may sound a little puzzling, but yes, this is true!

Hence, a beginner to machine learning needs to have a clear understanding that there are different roles that they can perform with machine learning skills. And accordingly the skill set that they should possess, would differ. This section will give clarity on machine learning skills that are needed to perform various machine learning roles.

Machine Learning Skills. Machine Learning Roles:- Machine Learning Engineer, Data Engineer, Machine Learning Scientist

Broadly, three main roles come into the picture when you talk about machine learning skills:

  1. Data Engineer
  2. Machine Learning Engineer
  3. Machine Learning Scientist

One must understand that data science, machine learning and artificial intelligence are interlinked. The following quote explains this better:

Data science produces insights. Machine learning produces predictions. Artificial intelligence produces actions.

A machine learning engineer is someone who deals with huge volumes of data to train a machine and impart it with knowledge that it uses to perform a specified task. However, in practice, there may be a little more to add to this:

Machine Learning RoleSkills RequiredRoles and Responsibilities
Data Engineer
  1. Python, R, and Databases
  2. Parallel and distributed 
  3. Knowledge on quality and reliability
  4. Virtual machines and cloud environment
  5. MapReduce and Hadoop
  1. Cleaning, manipulating and extracting the required data   
  2. Developing code for data analysis and manipulation
  3. Plays a major role in statistical analysis of data
Machine Learning Engineer
  1. Concepts of computer science and software engineering
  2. Data analysis and feature engineering
  3. Metrics involved in ML
  4. ML algorithm selection, and cross validation
  5. Math and Statistics
  1. Analyses and checks the suitability of an algorithm if it caters the needs of the current task
  2. Plays main role in deciding and selecting machine learning libraries for given task.
Machine Learning Scientist

Expert knowledge in:

  1. Robotics and Machine Learning
  2. Cognitive Science
  3. Engineering
  4. Mathematics and mathematical models
  1. Designs new models and algorithms of machine learning
  2. Researches intensively on machine learning and publishes their research papers.

Thus, gaining machine learning skills should be a task associated with clarity on the job role and of course the passion to learn them. As it is widely known, becoming a machine learning engineer is not a straightforward task like becoming a web developer or a tester.

Irrespective of the role, a learner is expected to have solid knowledge on data science. Besides, many other subjects are intricately intertwined in learning machine learning and for a learner it requires a lot of patience and zeal to learn skills and build them up as they move ahead in their career.

The following diagram shows the machine learning skills that are in demand year after year:

Demands of machine learning skills such as AI, TensorFlow, Apache Kafka, Data Science and AWS                                                                                                                                                                                                                                                                                                                                Image Source

In the coming sections, we would be discussing each of these skills in detail and how proficient you are expected to be in them.

Technical skills required to become ML Engineer

Top 8 Technical skills required to become a Machine Learning Engineer
Becoming a machine learning engineer means preparing oneself to handle interesting and challenging tasks that would change the way humanity is experiencing things right now. It demands both technical and non-technical expertise. Firstly, let’s talk about the technical skills needed for a machine learning engineer. Here is a list of technical skills a machine learning engineer is expected to possess:

  1. Applied Mathematics
  2. Neural Network Architectures
  3. Physics
  4. Data Modeling and Evaluation
  5. Advances Signal Processing Techniques
  6. Natural Language Processing
  7. Audio and video Processing
  8. Reinforcement Learning

Let us delve into each skill in detail now:

1.Applied Mathematics

Mathematics plays an important role in machine learning, and hence it is the first one on the list. If you wish to see yourself as a proven machine learning engineer, you ought to love math and be an expert in the following specializations of math.

  • But first let us understand why a machine learning engineer would need math at all? There are many scenarios where a machine learning engineer should depend on math. For example:
    • Choosing the right algorithm that suits the final needs
    • Understanding and working with parameters and their settings.
    • Deciding on validation strategies
    • Approximating the confidence intervals.

How much proficiency in Math does a machine learning engineer need to have?

It depends on the level at which a machine learning engineer works. The following diagram gives an idea about how important various concepts of math are for a machine learning enthusiast.

A) Linear algebra: 15%

B) Probability Theory and Statistics: 25%

C) Multivariate Calculus: 15%

D) Algorithms and Optimization: 15%

F) Other concepts: 10%

Data Source

Top 5 Maths concepts needed to become a Machine Learning Engineer

A) Linear Algebra

This concept plays a main role in machine learning. One has to be skilled in the following sub-topics of linear algebra:

  • Principal Component Analysis (PCA), Singular Value Decomposition (SVD)
  • Eigen decomposition of a matrix
  • LU Decomposition
  • QR Decomposition/Factorization
  • Symmetric Matrices
  • Orthogonalization & Orthonormalization
  • Matrix Operations
  • Projections
  • Eigenvalues & Eigenvectors
  • Vector Spaces and Norms

B) Probability Theory and Statistics

The core aim of machine learning is to reduce the probability of error in the final output and decision making of the machine. Thus, it is no wonder that probability and statistics play a major role.

The following topics are important in these subjects:

  • Combinatorics
  • Probability Rules & Axioms
  • Bayes’ Theorem
  • Random Variables
  • Variance and Expectation
  • Conditional and Joint Distributions
  • Standard Distributions (Bernoulli, Binomial, Multinomial, Uniform and Gaussian)
  • Moment Generating Functions, Maximum Likelihood Estimation (MLE)
  • Prior and Posterior
  • Maximum a Posteriori Estimation (MAP)
  • Sampling Methods.

C) Calculus

In calculus, the following concepts have notable importance in machine learning:

  • Integral Calculus
  • Partial Derivatives,
  • Vector-Values Functions
  • Directional Gradient
  • Hessian, Jacobian, Laplacian and Lagrangian Distributions.

D) Algorithms and Optimization

The scalability and the efficiency of computation of a machine learning algorithm depends on the chosen algorithm and optimization technique adopted. The following areas are important from this perspective:

  • Data structures (Binary Trees, Hashing, Heap, Stack etc)
  • Dynamic Programming
  • Randomized & Sublinear Algorithm
  • Graphs
  • Gradient/Stochastic Descents
  • Primal-Dual methods

E) Other Concepts

Besides, the ones mentioned above, other concepts of mathematics are also important for a learner of machine learning. They are given below:

  • Real and Complex Analysis (Sets and Sequences, Topology, Metric Spaces, Single-Valued and Continuous Functions Limits, Cauchy Kernel, Fourier Transforms)
  • Information Theory (Entropy, Information Gain)
  • Function Spaces and Manifolds

2.Neural Network Architectures

Neural networks are the predefined set of algorithms for implementing machine learning tasks. They offer a class of models and play a key role in machine learning.

The following are the key reasons why a machine learning enthusiast needs to be skilled in neural networks:

  • Neural networks let one understand how the human brain works and help to model and simulate an artificial one.
  • Neural networks give a deeper insight of parallel computations and sequential computations

Top 8 Areas of Neural networks that are important for Machine Learning

The following are the areas of neural networks that are important for machine learning:

  • Perceptrons
  •  Convolutional Neural Networks
  •  Recurrent Neural Network
  • Long/Short Term Memory Network (LSTM)
  • Hopfield Networks
  •  Boltzmann Machine Network
  • Deep Belief Network
  • Deep Auto-encoders

3.Physics

Having an idea of physics definitely helps a machine learning engineer. It makes a difference in designing complex systems and is a skill that is a definite bonus for a machine learning enthusiast.

4.Data Modeling and Evaluation

A machine learning has to work with huge amounts of data and leverage them into predictive analytics. Data modeling and evaluation is important in working with such bulky volumes of data and estimating how good the final model is.

For this purpose, the following concepts are worth learnable for a machine learning engineer:

  • Classification Accuracy
  • Logarithmic Loss
  • Confusion Matrix
  • Area under Curve
  • F1 Score
  • Mean Absolute Error
  • Mean Squared Error

5.Advanced Signal Processing Techniques

The crux of signal processing is to minimize noise and extract the best features of a given signal.

For this purpose, it uses certain concepts such as:

  • convex/greedy optimization theory and algorithms
  • spectral time-frequency analysis of signals
  • Algorithms such as wavelets, shearlets, curvelets, contourlets, bandlets, etc.

All these concepts find their application in machine learning as well.

6. Natural language processing

Natural language Processing image

The importance of natural language processing in artificial intelligence and machine learning is not to be forgotten. Various libraries and techniques of natural language processing used in machine learning are listed here:

  • Gensim and NLTK
  • Word2vec
  • Sentiment analysis
  • Summarization

7. Audio and Video Processing

This differs from natural language processing in the sense that we can apply audio and video processing on audio signals only. For achieving this, the following concepts are essential for a machine learning engineer:

  • Fourier transforms
  • Music theory
  • TensorFlow

8. Reinforcement Learning

Though reinforcement learning plays a major role in learning and understanding deep learning and artificial intelligence, it is good for a beginner of machine learning to know the basic concepts of reinforcement learning.

Programming skills required to become ML Engineer

5 Major Programming skills required to become a Machine Learning Engineer

Machine learning, ultimately, is coding and feeding the code to the machines and getting them to do the tasks we intend them to do. As such, a machine learning engineer should have hands-on expertise in software programming and related concepts. Here is a list of programming skills a machine learning engineer is expected to have knowledge on:

  1. Computer Science Fundamentals and Programming
  2. Software Engineering and System Design
  3. Machine Learning Algorithms and Libraries
  4. Distributed computing
  5. Unix

Let us look into each of these programming skills in detail now:

1.Computer Science Fundamentals and Programming

It is important that a machine learning engineer apply the concepts of computer science and programming correctly as the situation demands. The following concepts play an important role in machine learning and are a must on the list of the skillsets a machine learning engineer needs to have:

  • Data structures (stacks, queues, multi-dimensional arrays, trees, graphs)
  • Algorithms (searching, sorting, optimization, dynamic programming)
  • Computability and complexity (P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc.)
  • Computer architecture (memory, cache, bandwidth, deadlocks, distributed processing, etc.)

2.Software Engineering and System Design

Whatever a machine learning engineer does, ultimately it is a piece of software code – a beautiful conglomerate of many essential concepts and the one that is entirely different from coding in other software languages.

Hence, it is quintessential that a machine learning engineer have solid knowledge of the following areas of software programming and system design:

  • Scaling algorithms with the size of data
  • Basic best practices of software coding and design, such as requirement analysis, version control, and testing.
  • Communicating with different modules and components of work using library calls, REST APIs and querying through databases.
  • Best measures to avoid bottlenecks and designing the final product such that it is user-friendly.

3. Machine Learning Algorithms and Libraries

A machine learning engineer may need to work with multiple packages, libraries, algorithms as a part of day-to-day tasks. It is important that a machine learning engineer is well-versed with the following aspects of machine learning algorithms and libraries:

A thorough idea of various learning procedures including linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods.

  • Sound knowledge in packages and APIs such as scikit-learn, Theano, Spark MLlib, H2O, TensorFlow, etc.
  • Expertise in models such as decision trees, nearest neighbor, neural net, support vector machine and a knack to deciding which one fits the best.
  • Deciding and choosing hyperparameters that affect learning model and the outcome.
  • Comfortable to work with concepts such as gradient descent, convex optimization, quadratic programming, partial differential equations.
  • Select an algorithm which yields the best performance from random forests, support vector machines (SVMs), and Naive Bayes Classifiers, etc.

4   Distributed Computing 

Working as a machine learning engineer means working with huge sets of data, not just focused on one isolated system, but spread among a cluster of systems. For this purpose, it is important that a machine learning engineer knows the concepts of distributed computing.

5. Unix

Most clusters and servers that machine learning engineers need to work are variants of Linux(Unix). Though randomly they work on Windows and Mac, more than half of the time, they need to work on Unix systems only. Hence having sound knowledge on Unix and Linux is a key skill to become a machine learning engineer.

Programming Languages for Machine Learning

List of top 11 Programming Languages for Machine Learning

Machine learning engineers need to code to train machines. Several programming languages can be used to do this. The list of programming languages that a machine learning expert should essentially know are as under:

  1. C, C++ and Java
  2. Spark and Hadoop
  3. R Programming
  4. Apache Kafka
  5. Python
  6. Weka Platform
  7. MATLAB/Octave

In this section, let us know in detail why each of these programming languages is important for a machine learning engineer:

1.C, C++ and Java

These languages give essentials of programming and teach many concepts in a simple manner that form a foundation stone for working on complex programming patterns of machine learning. Knowledge of C++ helps to improve the speed of the program, while Java is needed to work with Hadoop and Hive, and other tools that are essential for a machine learning engineer.

2.Spark and Hadoop

Hadoop skills are needed for working in a distributed computing environment. Spark, a recent variant of Hadoop is gaining popularity among the machine learning tribe. It is a framework to implement machine learning on a large scale.

3.R Programming

R is a programming language built by statisticians specifically to work with programming that involves statistics. Many mathematical computations of machine learning are based on statistics; hence it is no wonder that a machine learning engineer needs to have sound knowledge in R programming.

4.Apache Kafka

Apache Kafka concepts such as Kafka Streams and KSQL play a major role in pre-processing of data in machine learning. Also, a sound knowledge of Apache Kafka lets a machine learning engineer to design solutions that are both multi-cloud based or hybrid cloud-based.  Other concepts such as business information such as latency and model accuracy are also from Kafka and find use in Machine learning.

5.Python

Of late, Python has become the unanimous programming language for machine learning. In fact, experts quote that humans communicate with machines through Python language.

Why Python is preferred for Machine Learning?

Python Programming Language has several key features and benefits that make it the monarch of programming languages for machine learning:

  • It is an all-in-one purpose programming language that can do a lot more than dealing with statistics.
  • It is beginner friendly and easy to learn.
  • It boasts of rich libraries and APIs that solve various needs of machine learning pretty easily.
  • Its productivity is higher than its other counterparts.
  • It offers ease of integration and gets the workflow smoothly from the designing stage to the production stage.

Python EcoSystem

There are various components of Python that make it preferred language for machine learning. Such components are discussed below:

  1. Jupyter Notebook
  2. Numpy
  3. Pandas
  4. Scikit-Learn
  5. TensorFlow

Various components of Python Ecosytem. Jupyter Notebook, NumPy, Pandas, Scikit-Learn, TensorFlow

1.Jupyter Notebook

Jupyter offers excellent computational environment for Python based data science applications. With the help of Jupyter notebook, a machine learning engineer can illustrate the flow of the process step-by-step very clearly.

2.NumPy

NumPy or Numerical Python is one of the components of Python that allows the following operations of machine learning in a smooth way:

  • Fourier transformation
  • Linear algebraic operations
  • Logical and numerical operations on arrays.

Of late, NumPy is gaining attention because it makes an excellent substitute to MATLAB, as it coordinates with Matplotlib and SciPy very smoothly.

3.Pandas

Pandas is a Python library that offers various features for loading, manipulating, analysing, modeling and preparing data. It is entirely dedicated for data analysis and manipulation.

4.Scikit-learn

Built on NumPy, SciPy, and Matplotlib, it is an open-source library of Python. It offers excellent features and functionalities for major aspects of machine learning such as clustering, dimensionality reduction, model reduction, regression and classification.

5.TensorFlow

TensorFlow is another framework of Python. It finds its usage in deep learning and having a knowledge of its libraries such as Keras, helps a machine learning engineer to move ahead confidently in their career.

6.Weka Platform

It is widely known that machine learning is a non-linear process that involves many iterations. Weka or Waikato Environment for Knowledge Analysis is a recent platform that is designed specifically designed for applied machine learning. This tool is also slowing gaining its popularity and thus is a must-include on the list of skills for a machine learning engineer.

7.MATLAB/Octave

This is a basic programming language that was used for simulation of various engineering models. Though not popularly used in machine learning, having sound knowledge in MATLAB lets one learns the other mentioned libraries of Python easily.

Soft skills or behavioural skills required to become ML engineer

Top 6 Soft skills required to become a Machine Learning engineer.

Technical skills are relevant only when they are paired with good soft skills. And the machine learning profession is no exception to this rule. Here is a list of soft skills that a machine learning engineer should have:

  1. Domain knowledge
  2. Communication Skills
  3. Problem-solving skills
  4. Rapid prototyping
  5. Time management
  6. Love towards constant learning

Let us move ahead and discuss how each of these skills make a difference to a machine learning engineer.

1.Domain knowledge

Machine learning is such a subject that needs the best of its application in real-time. Choosing the best algorithm while solving a machine learning problem in your academia is far different from what you do in practice. Various aspects of business come into picture when you are a real-time machine learning engineer. Hence, a solid understanding of the business and domain of machine learning is of utmost importance to succeed as a good machine learning engineer.

2.Communication Skills

As a machine learning engineer, you need to communicate with offshore teams, clients and other business teams. Excellent communication skills are a must to boost your reputation and confidence and to bring up your work in front of peers.

3.Problem-solving skills

Machine learning is all about solving real time challenges. One must have good problem-solving skills and be able to weigh the pros and cons of the given problem and apply the best possible methods to solve it.

4.Rapid Prototyping

Choosing the correct learning method or the algorithm are signs of a machine learning engineer’s good prototyping skills. These skills would be a great saviour in real time as they would show a huge impact on budget and time taken for successfully completing a machine learning project.

5.Time management

Training a machine is not a cake-walk. It takes huge time and patience to train a machine. But it’s not always that machine learning engineers are allotted ample time for completing tasks. Hence, time management is an essential skill a machine learning professional should have to effectively deal with bottlenecks and deadlines.

6.Love towards constant learning

Since its inception, machine learning has witnessed massive change – both in the way it is implemented and in its final form. As we have seen in the previous section, technical and programming skills that are needed for machine learning are constantly evolving. Hence, to prove oneself a successful machine learning expert, it is very crucial that they have a zeal to update themselves – constantly!

Conclusion

The skills that one requires to begin their journey in machine learning are exactly what we have discussed in this post. The future for machine learning is undoubtedly bright with companies ready to offer millions of dollars as remuneration, irrespective of the country and the location.

Machine learning and deep learning will create a new set of hot jobs in the next five years. – Dave Waters

All it takes to have an amazing career in machine learning is a strong will to hone one’s skills and gain a solid knowledge of them. All the best for an amazing career in machine learning!

Priyankur

Priyankur Sarkar

Data Science Enthusiast

Priyankur Sarkar loves to play with data and get insightful results out of it, then turn those data insights and results in business growth. He is an electronics engineer with a versatile experience as an individual contributor and leading teams, and has actively worked towards building Machine Learning capabilities for organizations.

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4 comments

robin 21 Jun 2019

Your article had helped me a lot in learning indepth concepts of Machine learning,keep up the good work.

Rahul sharma 06 Aug 2019

The nice article very good and explained in an easy understanding way thanks to the author...

venkat k 06 Aug 2019

I have been surfing online more than 3 hours lately, yet I by no means discovered any interesting article like yours. Its beautiful value was sufficient for me. In my view, if all webmasters and bloggers made excellent content as you probably did, the internet can be a lot more helpful than ever before.

Bhavana 06 Aug 2019

One of my friend shared this article. I really loved the article and I started subscribing for Knowledgehut, please update me for the upcoming articles related to the machine learning...

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Data Science: Correlation vs Regression in Statistics

In this article, we will understand the key differences between correlation and regression, and their significance. Correlation and regression are two different types of analyses that are performed on multi-variate distributions of data. They are mathematical concepts that help in understanding the extent of the relation between two variables: and the nature of the relationship between the two variables respectively. Correlation Correlation, as the name suggests is a word formed by combining ‘co’ and ‘relation’. It refers to the analysis of the relationship that is established between two variables in a given dataset. It helps in understanding (or measuring) the linear relationship between two variables.  Two variables are said to be correlated when a change in the value of one variable results in a corresponding change in the value of the other variable. This could be a direct or an indirect change in the value of variables. This indicates a relationship between both the variables.  Correlation is a statistical measure that deals with the strength of the relation between the two variables in question.  Correlation can be a positive or negative value. Positive Correlation Two variables are considered to be positively correlated when the value of one variable increases or decreases following an increase or decrease in the value of the other variable respectively.  Let us understand this better with the help of an example: Suppose you start saving your money in a bank, and they offer some amount of interest on the amount you save in the bank. The more the amount you store in the bank, the more interest you get on your money. This way, the money stored in a bank and the interest obtained on it are positively correlated. Let us take another example: While investing in stocks, it is usually said that higher the risk while investing in a stock, higher is the rate of returns on such stocks.  This shows a direct inverse relationship between the two variables since both of them increase/decrease when the other variable increases/decreases respectively. Negative Correlation Two variables are considered to be negatively correlated when the value of one variable increases following a decrease in the value of the other variable. Let us understand this with an example: Suppose a person is looking to lose weight. The one basic idea behind weight loss is reducing the number of calorie intake. When fewer calories are consumed and a significant number of calories are burnt, the rate of weight loss is quicker. This means when the amount of junk food eaten is decreased, weight loss increases. Let us take another example: Suppose a popular non-essential product that is being sold faces an increase in the price. When this happens, the number of people who purchase it will reduce and the demand would also reduce. This means, when the popularity and price of the product increases, the demand for the product reduces. An inverse proportion relationship is observed between the two variables since one value increases and the other value decreases or one value decreases and the other value increases.  Zero Correlation This indicates that there is no relationship between two variables. It is also known as a zero correlation. This is when a change in one variable doesn't affect the other variable in any way. Let us understand this with the help of an example: When the increase in height of our friend/neighbour doesn’t affect our height, since our height is independent of our friend’s height.  Correlation is used when there is a requirement to see if the two variables that are being worked upon are related to each other, and if they are, what the extent of this relationship is, and whether the values are positively or negatively correlated.  Pearson’s correlation coefficient is a popular measure to understand the correlation between two values.  Regression Regression is the type of analysis that helps in the prediction of a dependant value when the value of the independent variable is given. For example, given a dataset that contains two variables (or columns, if visualized as a table), a few rows of values for both the variables would be given. One or more of one of the variables (or column) would be missing, that needs to be found out. One of the variables would depend on the other, thereby forming an equation that relevantly represents the relationship between the two variables. Regression helps in predicting the missing value. Note: The idea behind any regression technique is to ensure that the difference between the predicted and the actual value is minimal, thereby reducing the error that occurs during the prediction of the dependent variable with the help of the independent variable. There are different types of regression and some of them have been listed below: Linear Regression This is one of the basic kinds of regression, which usually involves two variables, where one variable is known as the ‘dependent’ variable and the other one is known as an ‘independent’ variable. Given a dataset, a pattern has to be formed (linear equation) with the help of these two variables and this equation has to be used to fit the given data to a straight line. This straight-line needs to be used to predict the value for a given variable. The predicted values are usually continuous. Logistic Regression There are different types of logistic regression:  Binary logistic regression is a regression technique wherein there are only two types or categories of input that are possible, i.e 0 or 1, yes or no, true or false and so on. Multinomial logistic regression helps predict output wherein the outcome would belong to one of the more than two classes or categories. In other words, this algorithm is used to predict a nominal dependent variable. Ordinal logistic regression deals with dependant variables that need to be ranked while predicting it with the help of independent variables.  Ridge Regression It is also known as L2 regularization. It is a regression technique that helps in finding the best coefficients for a linear regression model with the help of an estimator that is known as ridge estimator. It is used in contrast to the popular ordinary least square method since the former has low variance and hence it calculates better coefficients. It doesn’t eliminate coefficients thereby not producing sparse, simple models.  Lasso Regression LASSO is an acronym that stands for ‘Least Absolute Shrinkage and Selection Operator’. It is a type of linear regression that uses the concept of ‘shrinkage’. Shrinkage is a process with the help of which values in a data set are reduced/shrunk to a certain base point (this could be mean, median, etc). It helps in creating simple, easy to understand, sparse models, i.e the models that have fewer parameters to deal with, thereby being simple.  Lasso regression is highly suited for models that have high collinearity levels, i.e a model where certain processes (such as model selection or parameter selection or variable selection) is automated.  It is used to perform L1 and L2 regularization. L1 regularization is a technique that adds a penalty to the given values of coefficients in the equation. 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A Peak Into the World of Data Science

Touted as the sexiest job in the 21st century, back in 2012 by Harvard Business Review, the data science world has since received a lot of attention across the entire world, cutting across industries and fields. Many people wonder what the fuss is all about. At the same time, others have been venturing into this field and have found their calling.  Eight years later, the chatter about data science and data scientists continues to garner headlines and conversations. Especially with the current pandemic, suddenly data science is on everyone’s mind. But what does data science encompass? With the current advent of technology, there are terabytes upon terabytes of data that organizations collect daily. From tracking the websites we visit - how long, how often - to what we purchase and where we go - our digital footprint is an immense source of data for a lot of businesses. Between our laptops, smartphones and our tablets - almost everything we do translates into some form of data.  On its own, this raw data will be of no use to anyone. Data science is the process that repackages the data to generate insights and answer business questions for the organization. Using domain understanding, programming and analytical skills coupled together with business sense and know-how, existing data is converted to provide actionable insights for an organization to drive business growth. The processed data is what is worth its weight in gold. By using data science, we can uncover existing insights and behavioural patterns or even predict future trends.  Here is where our highly-sought-after data scientists come in.  A data scientist is a multifaceted role in an organization. They have a wide range of knowledge as they need to marry a plethora of methods, processes and algorithms with computer science, statistics and mathematics to process the data in a format that answers the critical business questions meaningfully and with actionable insights for the organization. With these actionable data, the company can make plans that will be the most profitable to drive their business goals.  To churn out the insights and knowledge that everyone needs these days, data science has become more of a craft than a science despite its name. The data scientists need to be trained in mathematics yet have some creative and business sense to find the answers they are looking in the giant haystack of raw data. They are the ones responsible for helping to shape future business plans and goals.  It sounds like a mighty hefty job, doesn’t it? It is also why it is one of the most sought after jobs these days. The field is rapidly evolving, and keeping up with the latest developments takes a lot of dedication and time, in order to produce actionable data that the organizations can use.  The only constant through this realm of change is the data science project lifecycle. We will discuss briefly below on the critical areas of the project lifecycle. The natural tendency is to envision that it is a circular process immediately - but there will be a lot of working back and forth within some phases to ensure that the project runs smoothly.  Stage One: Business Understanding  As a child, were you one of those children that always asked why? Even when the adults would give you an answer, you followed up with a “why”? Those children will have probably grown up to be data scientists as it seems, their favourite question is: Why? By asking the why - they will get to know the problem that needs to be solved and the critical question will emerge. Once there is a clear understanding of the business problem and question, then the work can begin. Data scientists want to ensure that the insights that come from this question are supported by data and will allow the business to achieve the desired results. Therefore, the foundation stone to any data science project is in understanding the business.  Stage Two: Data Understanding  Once the problem and question have been confirmed, you need to start laying out the objectives of this project by determining the required variables to be predicted. You must know what you need from the data and what the data should address. You must collate all the information and data, which can be reasonably difficult. An agreement over the sources and the requirements of the data characteristics needs to be reached before moving forward.  Through this process, an efficient and insightful understanding is required of how the data can and will be used for the project. This operational management of the data is vital, as the data that is sourced at this stage will define the project and how effective the solutions will be in the end.  Stage Three: Data Preparation  It has been said quite often that a bulk of a data scientist’s time is spent in preparing the data for use. In this report from CrowdFlower in 2016, the percentage of time spent on cleaning and organizing data is pegged at 60%. That is more than half their day!  Since data comes in various forms, and from a multitude of sources, there will be no standardization or consistency throughout the data. Raw data needs to be managed and prepared - with all the incomplete values and attributes fixed, and all deconflicting values in the data eliminated. This process requires human intervention as you must be able to discern which data values are required to reach your end goal. If the data is not prepared according to the business understanding, the final result might not be suitable to address the issue.  Stage Four: Modeling Once the tedious process of preparation is over, it is time to get the results that will be required for this project lifecycle. There are various types of techniques that can be used, ranging from decision-tree building to neural network generation. You must decide which would be the best technique based on the question that needs to be answered. If required, multiple modeling techniques can be used; where each task must be performed individually. Generally, modeling techniques are applied more than once (per process), and there will be more than one technique used per project.  With each technique, parameters must be set based on specific criteria. You, as the data scientist, must apply your knowledge to judge the success of the modeling and rank the models used based on the results; according to pre-set criteria. Stage Five: Evaluation Once the results are churned out and extracted, we then need to refer back to the business query that we talked about in Stage One and decide if it answers the question raised; and if the model and data meet the objectives that the data science project has set out to address. The evaluation also can unveil other results that are not related to the business question but are good points for future direction or challenges that the organization might face. These results should be tabled for discussion and used for new data science projects. Final Stage: Deployment  This is almost the finishing line!  Now with the evaluated results, the team would need to sit down and have an in-depth discussion on what the data shows and what the business needs to do based on the data. The project team should come up with a suitable plan for deployment to address the issue. The deployment will still need to be monitored and assessed along the way to ensure that the project will be a successful one; backed by data.  The assessment would normally restart the project lifecycle; bringing you full circle.  Data is everywhere  In this day and age, we are surrounded by a multitude of data science applications as it crosses all industries. We will focus on these five industries, where data science is making waves. Banking & Finance  Financial institutions were the earliest adopters of data analytics, and they are all about data! From using data for fraud or anomaly detection in their banking transactions to risk analytics and algorithmic trading - one will find data plays a key role in all levels of a financial institution.  Risk analytics is one of the key areas where data science is used; as financial institutions depend on it to make strategic decisions for the financial health of the business. They need to assess each risk to manage and optimize their cost.  Logistics & Transportation  The world of logistics is a complex one. In a production line, raw materials sometimes come from all over the world to create a single product. A delay of any of the parts will affect the production line, and the output of stock will be affected drastically. If logistical delays can be predicted, the company can adjust quickly to another alternative to ensure that there will be no gap in the supply chain, ensuring that the production line will function at optimum efficiency.  Healthcare  2020 has been an interesting one. It has been a battle of a lifetime for many of us. Months have passed, and yet the virus still rages on to wreak havoc on lives and economies. Many countries have turned to data science applications to help with their fight against COVID-19. With so much data generated daily, people and governments need to know various things such as:  Epidemiological clusters so people can be quarantined to stop the spread of the virus tracking of symptoms over thousands of patients to understand how the virus transmits and mutates to find vaccines and  solutions to mitigate transmission. Manufacturing  In this field, millions can be on the line each day as there are so many moving parts that can cause delays, production issues, etc. Data science is primarily used to boost production rates, reduce cost (workforce or energy), predict maintenance and reduce risks on the production floor.  This allows the manufacturer to make plans to ensure that the production line is always operating at the optimum level, providing the best output at any given time.  Retail (Brick & Mortar, Online)  Have you ever wondered why some products in a shop are placed next to each other or how discounts on items work? 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A Peak Into the World of Data Science

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Future Proof Your Career With Data Skills

Data is everywhere, and we have all seen exponential growth in the data that is generated daily. Information must be extracted from this data to make sense of it, and we must gain insights from this information that will help us to understand repeating patterns. Analysing these patterns will help us to know more about consumers and their behaviour, hence provide services and manufacture products that will benefit both the organization as well as the consumers. This is where Data Science comes into the picture. The art of analysing the data, extracting patterns, applying algorithms, tweaking the data to suit our requirements, and more – are all parts of data science. The field has seen massive growth in the last few years and this growth may not stop for the next 5 years at least. Some people are apprehensive about the future, but the opportunities in the field of data science are improving day by day, creating new paths for people to get in and contribute their expertise/experiences. What is Data Science? As mentioned previously, data is generated in large amounts daily. This data generation happens everywhere, ranging from a small organization to multi-national companies. This kind of data is also known as 'big data', given their specific characteristics in terms of volume, type of data and speed at which the data gets generated. It is important to make use of this big data by processing it into something useful so that the organizations can use advanced analytics and insights to their advantage (generating better profits, more customer-reach, and so on). Who is a Data Scientist? A data scientist is a person who is trained and experienced in working with data, i.e. data gathering, data cleaning, data preparation, data transformation, and data analysis. These steps will help understand the data, extract hidden patterns and put forward insights about the data. All these processes are done with the help of algorithms which are specially designed to perform a specific task. Many analyses have revealed that Data Scientist, Machine Learning Engineer, Artificial Intelligence Engineer are some of the most sought-after jobs. Not to forget the high pay that comes with it. Data science is an intricate combination of mathematics, statistics, analytics, and computer science. Mathematics & statistics are required to understand the ideas behind the algorithms and their working. On the other hand, analytics is associated with many data cleaning, transformation, preparation and analytics operations that are performed on the data with the help of computer science (programming languages). All these skills (which a data scientist possesses) will help the businesses to thrive. Data scientists are usually those who are able to find out why things work the way they do, why they don’t work as expected, what has gone wrong in the business and how it can be fixed. All these are different processes in the world of data analytics. They would also have to interact with potential stakeholders, discuss business challenges and help improve it.  What would a day in the life of a Data Scientist look like? If the general idea of stand-up meetings and sprint meetings is not taken into consideration, a day in the life of a data scientist would revolve around gathering data, understanding it, talking to relevant people about the data, asking questions about it, reiterating the requirement and the end product, and working on how it can be achieved. It looks like this: Data collection This part deals with the collection of raw data from various resources. These resources include websites, various social media platforms, people’s profiles, and so on. All this data needs to be collected and stored in a place which is easy to access while working with the data. Data cleaning This is considered as one of the most important steps in data science. This is because good data yields great results, whereas noisy, unclean, missing and redundant data yields unsatisfactory results. Once raw data has been collected, it needs to be accessed and cleaned by various methods. Redundant rows or columns have to be deleted, missing data either needs to be filled or deleted, irrelevant columns have to be eliminated, and so on. Data transformations In this step, the data (which is usually in the form of row x column) is converted into a format that is required by the algorithm to process upon. For example- a text analysis task may require data in the form of text whereas a prediction or regression problem may require data in the form of a table, i.e. rows and columns. Based on the requirement and the end product, data has to be transformed into the respective formats. Using statistics, machine learning algorithms to solve the problem and extract insights The basics of statistics are considered to be a foundation while working as a data scientist. Understanding distributions, priors probabilities, posteriors probabilities, Bayesian theorem serve as a foundation while working with the data. The data needs to be interpreted and mangled with the help of statistics. It helps understand and solve problems by helping the data scientist extract meaningful and relevant insights/patterns from data.  Up-to-date sector knowledge It is important to stay up-to-date, know the new trends, packages, frameworks, new releases and changes that occur frequently if not on a daily basis. It is important to adapt and use whatever revolutionary technology comes our way and seems to be helpful in the specific scenario. It is essential to stay on top by knowing new algorithms, techniques, data mining algorithms, and so on.  It is important to keep learning, revising your career plan and update the skills that are necessary for the current world. Updating knowledge is vital to pursue future opportunities and make sure that your career path is aligned with your personal interests.  What about the salary? Salaries for Data Scientists are on the higher end of the spectrum, with a mean salary of about £60,000. With experience and constantly upgrading the skills, the salary can go up to £100,000 too. This also depends on the organization. If it is a start-up or a new company, they might not pay as much, but as and when the company grows, the pay-out increases. On similar lines, experience increases along with skills, which would make a data scientist more valuable to the organization.  Analytics has revealed that the number of data science-related jobs will see a surge and it has also been labelled as the 'sexiest job of the 21st century’. The demand is growing steadily. Almost every organization wishes to have a machine learning wing where data scientists would be much needed.  What are the pre-requisites to becoming a Data Scientist? If it is one of the companies from FAANG – Facebook, Amazon, Apple, Netflix, Google, it is not really a requirement to have a bachelor's degree or anything. There might be certain positions that require specific qualifications, but entry-level positions don't usually have any specific requirement of a degree. These companies certainly expect the data scientists to be hands-on in one or two programming languages (object-oriented such as C++ or Java, and Python). They might also require knowing specific frameworks (TensorFlow, Keras), deep learning algorithms (Neural networks, convolutional neural networks, recurrent neural networks), NumPy, Pandas and so on.  Machine learning is a concept which data scientists will have to be familiar with. This doesn’t mean just the definitions. It involves understanding the algorithms, the mathematical working behind it, the kind of results it would provide, the kind of cases where certain algorithms can be used and how the output can be improved by tweaking certain parameters present in the algorithm.  It is also essential to understand where Machine Learning can be used, and how it plays an important role in understanding the data, and prediction as well.  It never harms to get a bachelor’s degree, a master’s degree and a PhD. All these degrees add formidable value to the knowledge already gained.  How do I start on my Data Science Journey? Any job requires a resume where the relevant skills are mentioned in the right way and format. It is important to present yourself in the right way, and also exhibit the enthusiasm of learning and being updated. An entry-level data scientist job will require the basics of object-oriented programming, Python, scientific computing packages, basics of machine learning, statistics, analytics and hands-on programming abilities. Taking up certain foundational courses, working hands-on in projects, internships and group projects also help in providing a considerable amount of experience around working with data. It is also important to have a niche for data, be able to play around with data, extract patterns, have an eye out for insights, packages that could be used, the approach and so on.  Conclusion Technology will create new jobs, but Data Science and Artificial Intelligence will be a major part of our life in the upcoming years. This means some jobs may be lost too (because many processes which seem too trivial would be automated) but think about the new jobs that would be created! Instead of worrying about the jobs that would be lost due to AI replacing their work, it is essential to foresee and adapt to it. We have seen technologies revolutionizing the current world, and all this has happened because of ‘change’, because of how people have foreseen the circumstances and have adapted to it. For example, AI will not replace a doctor. But AI can replace a doctor without AI knowledge. We need to be up-to-date with the current trends, the technologies and the new and ever-changing requirements of the real world. The focus should be on learning to work with machines, not outwork them. 
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