Data Science Course with Python

The top skills that are needed to become a data scientist include the following:

1. Python Coding
2. R Programming
3. Hadoop Platform
4. SQL database and coding
5. Machine Learning and Artificial Intelligence
6. Apache Spark
7. Data Visualization
8. Unstructured data

1. Python Coding: Python is one of the most common and popular programming languages used in the field of data science. Owing to the versatility as well as the simplicity that Python offers, it takes various formats of data and helps in the processing of this data. Python also allows data scientists to create datasets as well as perform various operations on a dataset.
   
   
2. R Programming: Comprehensive knowledge of at least one analytical tool is preferred while embarking on a journey to become a master Data Scientist. Knowledge of R programming is usually an advantage for data scientists in order to make any data science problem easier to solve.
   
   
3. Hadoop Platform: Strictly speaking, the Hadoop platform is not a requirement for data science, but is heavily preferred in several data science projects. A study of 3490 jobs on LinkedIn proves that Hadoop is still the leading skill requirement for a data science engineer.
   
   
4. SQL database and coding: SQL is a language that is specifically designed to help data scientists to access, communicate as well as work on data. It helps a data scientist gain insights into the structure and formation of a database. MySQL also possesses concise commands that save time and decrease the level of technical skills required to perform operations on a database.
   
   
5. Machine Learning and Artificial Intelligence: Proficiency in the areas of Machine Learning and Artificial Intelligence is now a prerequisite for the pursuit of a career in Data Science. The knowledge and concepts of Machine Learning and Artificial Intelligence that a potential data scientist must be familiar with include the following:
1. Reinforcement Learning
2. Neural Network
3. Adversarial learning
4. Decision trees
5. Machine Learning algorithms
6. Logistic regression etc.
6. Apache Spark: One of the most popular data sharing technologies worldwide, Apache Spark is a big data computation, not unlike Hadoop. The only difference between Apache Spark and Hadoop is that Apache Spark is faster, because of the fact that Hadoop reads and writes to the disk, whereas Spark makes caches of its computations in the system memory.Apache Spark, therefore, is a tool used to help the data science algorithms run faster. It also aids in the dissemination of data processing when dealing with a large data set as well as in the handling of complex unstructured data sets. Apache Spark also aids Data Scientists in preventing the loss of data. Its benefit also lies in the speed with which it operates, as well as the ease with which a data scientist can carry out a project.
   
   
7. Data Visualization: A data scientist is expected to be able to visualize the data with the help of Visualization tools such as d3.js, Tableau, ggplot and matplotlib. These tools aid a data scientist in the conversion of complex results obtained as a result of processes performed on a data set and help to convert them into a format that is easy to understand and comprehend.Data visualization also gives organizations the opportunity to work directly with data. It also enables data scientists to quickly grasp insights from a particular data and outcome as well as enable them to act on the new outcome that is obtained.
   
   
8. Unstructured data: It is important for a data scientist to be able to work with unstructured data, which is content that is not labelled and organized into database values. Examples of unstructured data include videos, social media posts, audio samples, customer reviews, blog posts etc.

Below are the top 4 behavioral traits of a successful Data Scientist -

• Curiosity – Since they are dealing with massive amounts of data every single day, they should have an undying hunger for knowledge to keep them going.
  
  
• Clarity – Data Science is for you if you find yourself constantly asking "why" and "so what".  Whether cleaning up data or writing code, you should know what you are doing and why you're doing it.
  
  
• Creativity - Creativity in data science can be anything from finding innovative ways to visualize data, development of new tools or new modeling features. You need to be able to figure out what's missing and what needs to be included in order to get results.
  
  
• Skepticism – This is the differentiator between other creative minds and a data scientist. Data scientist need skepticism to keep their creativity in check. Skepticism keeps them in the real world rather than letting them getting carried away with creativity.

There are many benefits to being in the job declared as the ‘Sexiest job of the 21st century’ by Harvard Business review:

1. High Pay: First things first, we all expect high pay from a job, especially when the qualification bar is set incredibly high. Due to high demand and low supply, data scientist jobs are one of the highest paying jobs in the IT industry today.
   
   
2. Good bonuses: Although it is a part of their pay, data scientists can expect impressive bonuses. Other perks may include equity shares and signing perks.
   
   
3. Education: By the time you become a data scientist, you would probably be having either a Masters or a PhD due to the demand for knowledge in this field. You could receive offers to work as a lecturer or as a researcher for governmental as well as private institutions.
   
   
4. Mobility: Many businesses that collect data are mostly located in developed countries. Getting a job in one would fetch you a hefty salary as well as raise your standard of living.
   
   
5. Network: Your involvement in the tech world through research papers in international journals, tech talks at conferences and many more platforms would help expand your network of data scientists. This, in turn, can be used for referral purposes as well.

If you’re considering a career in data science, there are 3 educational paths that can help you get started.

1. The most preferred way is to get a degree. Even though getting a degree takes multiple years and can cost a lot of money, it offers significant advantages. Degree programs provide structure, internships, networking and recognized academic qualifications for your résumé.
   
   
2. Another way is to learn at your own pace. Online courses can help work through the material at your own pace. The projects you do are scheduled to suit your convenience.
   
   
3. And finally, there are Machine Learning Bootcamps. These are intense training workshops that combine theory with hands-on practicals. Their pace is way more rapid than the traditional degrees. The only drawback is that you won’t have a degree after your name.
   
   

A report published in May 2017 suggested that in a field like data science, academic qualifications are highly valuable. Reportedly 90% of interviewed data scientists reported to obtaining an advanced degree – 49% held a master's and 41% held a PhD.

Data science is a vast world with a large open source community with many sub-fields. As a beginner, you're bound to make a few mistakes. However, one of the most common mistakes that amateur data scientists make includes choosing a library that is not the most appropriate one for the task at hand.  

Many times rather than taking into consideration the type of data we have, constraints, and the aim of our project, we simply choose a library because it is the most popular one or one with a plethora of features. It is important to know that the most popular libraries are not always the ones which are best suited for our problem.  

Some of the other common mistakes include- 

• Not investing enough time to learn visualization and exploration of data. 
• Trying to use multiple data science tools all at once. 
• Not choosing tools according to the business requirements/constraints.

Data Science and Machine Learning go hand in hand. While Machine Learning is the ability of a machine to find patterns from data, Data Science is the mechanism by which the machines are provided with data. The more the availability of data, the more is the complexity and difficulty in compiling new predictive models that are able to accurately and efficiently work on this data. This is where the role of Machine Learning comes in, to help Data Scientists make sense of the large amounts of data they have and to convert it into meaningful information.

As a data scientist, you have to deal with all kinds of data-numbers, text, image, etc. Natural Language Processing (NLP) helps us deal with the textual form of data and use it in our computations and algorithms. Some of the important applications of NLP are:

• Sentiment analysis
• Part-of-speech tagging
• Machine translation
• Document generation and summarization
• Speech and character recognition

Below is the list of top business skills needed to become a data scientist:

1. Analytic Problem-Solving
2. Communication Skills
3. Intellectual Curiosity
4. Industry Knowledge

1. Analytic Problem-Solving – In order to find a solution, it is important to first understand and analyze what the problem is. To do that, a clear perspective and awareness of the right strategies are needed. 
   
   
2. Communication Skills – Communicating customer analytics or deep business to companies is one of the key responsibilities of data scientists.
   
   
3. Intellectual Curiosity: If you are not curious enough to get an answer to that "why", then data science is not for you. It’s the combination of curiosity and thirst to deliver results that offers great value to a commercial enterprise.
   
   
4. Industry Knowledge – Last, but not least, this is perhaps one of the most important skills. Having solid industry knowledge will give you a more clear idea of what needs attention and what needs to be ignored.

Data science may not be as much about communication as it is about data, but however good a data scientist is, he must remember that data science is not all about crunching numbers. One of the main responsibilities of a data scientist is to communicate customer analytics as well as business insights to his customers.Data scientists must also remember that no technology exists in a vacuum in the business environment of today. There always exists some level of integration between data, its applications as well as the people.Thus, being able to communicate with stakeholders is a skill that every data scientist must have. Communicating with and understanding the requirements of a customer is another key priority that requires a data scientist to have good communication skills.

From its use in enabling the faster analysis of information as well as the ease it offers in recognizing trends and patterns in a given set of data, data visualization is proving to be increasingly useful in the field of Data Science. No matter the size of the organization, every company with an eye towards the future is harnessing the power of data visualization. For the same reason, every company in the world, no matter how big or small, is looking for data visualization experts who can channel this power of data visualization and use it for the faster progress of the company. While other skills are also important in a data scientist, studies and surveys increasingly show that the ability of a data scientist to use and visualize data is a highly sought after skill in the job market these days, which is also a trend that is unlikely to stop in the foreseeable future.

The role of the data scientist is, no doubt, one of the hottest jobs in the market today and becoming a data scientist demands an ardent passion for knowledge. We have compiled a list of key points to help you decide whether data science is right for you or not.

• Good analytical skills: Without a doubt, you should have an avid interest in analyzing even simple things in real life.
  
  
• Mathematics: A Data scientist’s job involves manipulating numbers in the data, making sense of it and finding relations between the variables. Being comfortable working with statistics is extremely important.
  
  
• Coding skills: Coding is important to help you perform the tedious task of dealing with massive data, in real-time and to compute them in an appropriate manner.
  
  
• Continuous learning: A data scientist absolutely cannot stop learning data science as it requires lifelong practice of difficult and complex concepts.

Below are the best ways to brush up your data science skills for data scientist jobs:

• Boot camps: Boot camps are the perfect way to brush up your Python basics. They usually last anywhere from 4 to 5 days. These boot camps not only offer theoretical knowledge but also hands-on experience.
  
  
• MOOC courses: These are online courses and include some of the latest trends in the industry. These are taught by data science experts and help polish implementation skills in the form of assignments.
  
  
• Certifications: Certifications provide you with an additional skill set and help improve your CV significantly. Some of the famous data science certifications are:
  
  
  • Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
  • Cloudera Certified Associate - Data Analyst
  • Cloudera Certified Professional: CCP Data Engineer
    
    
• Projects: Projects help you explore new solutions to already answered questions depending upon the project constraints. The more you work on projects, the more refined your thinking and skills will be.
  
  
• Competitions: Lastly, competitions like Kaggle etc. help in improving your problem-solving skills with given restraints and force you to find an optimum solution satisfying all the requirements.

We live in a world of data. Your medical diagnosis is data, your investment in the stock market is data, your browsing history is data and so on. Most companies collect data for their own benefit and these data tend to improve our customer experience also. The data science job offered by companies determines what kind of companies they are:

• Small companies use Google Analytics for their analysis as they have fewer resources and fewer data to work with.
• Mid-size companies have data but would need someone to apply ML techniques on it to leverage it.
• Big companies already have teams of data scientists, so they would be needing a new data scientist with specialization. For eg: Visualization, ML expert etc.

The best way to master the art of Data Science is to practice and work your way through the challenges you face while solving Data Science problems. Some ways to practice your data science skills are to work on the following data science problems, categorized according to their difficulty level as compared to your expertise level:

• Beginner Level:
  
  
• Iris Data Set: The Iris Data Set is widely accepted to be the most popular, versatile, resourceful and easy data set in the field of pattern recognition. The Iris data set is said to be the easiest data set to incorporate during your learning of various classification techniques. This is the best data set for beginners to embark on their journey in the field of Data Science. The Iris Data Set consists of merely 4 columns and 50 rows. 
  
  Practice Problem: Predict the class of a flower on the basis of these parameters.
  
  
• Loan Prediction Data Set: The banking domain has the greatest use of data analytics and data science methodologies as compared to every other industry. The Loan Prediction data set provides the learner with a taste of working with the concepts that are applicable in the domain of banking and insurance - the challenges faced, the strategies implemented, the variables that influence the outcomes etc. The Loan prediction data set consists of 13 columns and 615 rows and is a classification problem data set.
  
  Practice Problem: Predict if a given loan will be approved by the bank or not.
  
  
• Bigmart Sales Data Set: Another industry that makes heavy use of analytics in order to optimize business processes is the Retail sector. Operations such as Product Bundling, offer customizations, inventory management etc are efficiently handled with the help of Data Science and Business Analytics. The Bigmart Sales Data Set is used in Regression problems and consists of 12 variables and 8523 rows.
  
  Practice Problem: Predict the sales of a retail store.

• Intermediate Level:
  
  
• Black Friday Data Set: The Black Friday Data Set comprises of sales transactions that were captured from a retail store. It is an apt data set in order to expand and explore engineering skills as well as to gain an understanding of the day to day shopping experiences of millions of customers. The Black Friday data set has 12 columns and 550,069 rows and is a regression problem.
  
  Practice Problem: Predict the amount of the total purchase made.
  
  
• Human Activity Recognition Data Set: The Human Activity Data Set has a collection of 30 human subjects that were collected via recordings by smartphones that were embedded with inertial sensors. The Human Activity Recognition Data Set consists of 561 columns and 10,299 rows.
  
  Practice Problem: Predict the human activity category.
  
  
• Text Mining Data Set: The Text Mining Data Set was originally obtained by the Siam Text Mining Competition that was held in 2007. This data set consists of aviation safety reports that describe the problems that were encountered on certain flights. The Text Mining Data Set consists of 30,438 and 21,519 columns and is a high dimensional and multi-classification problem.
  
  Practice Problem: Classify the documents on the basis of their labels.
  
  

• Advanced Level:
  
  
• Urban Sound Classification: Things like Titanic survival prediction etc are the very basic and simple Machine Learning problems that a beginner in the field of Machine Learning goes through. These Machine Learning problems, however, do not give a Machine Learner a taste of the real world problems. The Urban Sound Classification data set is the solution to the introduction and implementation of Machine Learning concepts to real world problems. It is a data set that consists of 8,732 sound clippings of urban sounds that can be categorized in 10 classes. The Urban Sound Classification problem introduces the developer to the concepts of audio processing in real world scenarios of classification. 
  
  Practice Problem: Classify the type of sound that is obtained from a particular audio.
  
  
• Identify the digits data set: This data set comprises of 7000 images, totalling 31MB, with dimensions of 28X28 each. It allows the developer to study, analyze and recognize the elements present in an image. 
  
  Practice Problem: Identify the digits present in a given image
  
  
• Vox Celebrity Data Set: Another important and developing field in the arena of Deep Learning is the concept of Audio Processing. The Vox Celebrity Data Set is meant for large scale speaker identification. It is a collection of words spoken by celebrities and extracted from YouTube videos. The Vox Celebrity Data Set makes for an interesting use case to be formed for the isolation and identification of speech recognition. This data set consists of 100,000 words spoken by 1,251 celebrities from around the world.
  
  Practice Problem: Identify the celebrity that a given voice belongs to.

Apache Spark is a general, multipurpose engine that is used for the processing of large scale data. It is an open source, in-memory distributed computing engine that was developed in the AMPLab at UC Berkeley. It is a computing engine which is highly versatile in any given environment. Apache Spark is basically an advanced analytical tool that is useful for the implementation of Machine Learning algorithms.Apache Spark is also 100 times faster as compared to Hadoop MapReduce in the system memory and 10 times faster on the disk. Apache Spark is seen by many experts as the answer to the problems and inefficiencies produced by the use of MapReduce. Some other reasons for the popularity of Apache Spark include the following:

1. Apache Spark is very well suited for use in the era of Big Data. This is mainly because it supports the development of applications of Big Data, also enabling the reuse of code across several types of applications such as interactive, streaming and batch applications.
   
   
2. It also enables developers to work together on a unified platform, as well as to execute Scala or Python across a cluster of networks, instead of an individual system.
   
   
3. Apache Spark allows users to load the data into the memory of a cluster and then query it repeatedly.
   
   
4. It enables high speed stream processing of data that has low latency.
   
   
5. It allows real time querying of data.
   
   
6. Apache Spark allows for a clear separation of importing data as well as distributed computation.
   
   
7. It is supported by a large number of major vendors including Intel, MapR, IBM, Hortonworks etc. among other major Big Data platforms.

The job of a Data Scientist has been declared as “The Sexiest Job of the 21st Century” by none other than Harvard Business Review. So how do you prepare for a career in data science? Don’t worry, we have compiled some of the key skills & steps required to get started.

1. Degree/certificate: Be it an online or offline classroom course, it is important to start with a basic course that covers the fundamentals. Not only will you learn how to apply cutting-edge tools but you will also get a boost in your career growth. Due to rapid advancements in the field, data science demands continuous learning and for the same reason, data scientists have more PhDs than any of the other job titles.
   
   
2. Unstructured data: The job of a data scientist boils down to discovering patterns in data. Usually, the data is unstructured and doesn’t fit into a database. This step has the highest complexity due to the sheer amount of work involved to structure the data and make it useful. Your job is to understand and manipulate this unstructured data.
   
   
3. Software and Frameworks: Due to the huge amount of unstructured data, it is essential that you are comfortable in using some of the most popular and useful software and frameworks to go along with an equally important programming language - preferably R.
   
   
1. Although R has a steep learning curve, it is the most used programming language to solve statistical problems. At least 43% of data scientists employ R language for their analysis.
   
   
2. Hadoop is the framework used by a majority of data scientists in situations when the amount of data is in excess compared to the memory at hand. In this case, Hadoop is used as it quickly conveys the data to various points on the machine. Spark is becoming the most popular framework after Hadoop. Like Hadoop, Spark is also used for computational work but is faster than its counterpart. It also helps in preventing the loss of data in data science which is sometimes the case in Hadoop.
   
   
3. cAfter learning the programming language and framework, it is important that we have in-depth knowledge of databases as well. It is expected from a data scientist that he/she is proficient in SQL queries.
   
   
4. Machine learning and Deep Learning: After gathering and preparing data, the next step is applying algorithms on it for better analysis. Through deep learning, we train our model to deal with the data we have provided it with.
   
   
5. Data visualization: Many data science projects require data scientists to help make informed business decisions with the analysis of the data, and data visualization. A data scientist’s job is to make the sense of huge amount of data given for analysis and provide it to the business in the form of graphs and charts. Some of the tools used for this purpose include matplotlib, ggplot2 etc.

Data scientists are some of the most educated professionals in the IT field. Almost 88% of data scientists hold a Master’s degree while 46% of all data scientists are PhD degree holders. While there exist notable expectations for this trend, a strong educational background is one of the most observed backgrounds in data scientists.

In order to become a Data Scientist, you may take a Bachelor’s degree in Social Sciences, Statistics, Computer Science or Physical Sciences. The most common backgrounds that Data Scientists possess in the order of their popularity include Mathematics and Statistics (32%), Computer Science (19%) and Engineering  (16%). After obtaining a Bachelor’s degree, most Data Scientists have either pursued a Master’s degree or PhD as well as have undertaken online training in a related field.

As mentioned before, almost 88% of data scientists hold a Master’s degree while 46% of all data scientists are PhD degree holders.

A degree is very important because of the following –

• Networking – While pursuing the degree, you will get the opportunity to make friends and acquaintances. In any field, networking is one of the major assets.
  
  
• Structured learning – Following a particular schedule and keeping up with the curriculum is more effective and beneficial than doing things unplanned.
  
  
• Internships – Another very major aspect is the practical hands-on experience you get through internship.
  
  
• Recognized academic qualifications for your résumé – A degree from a prestigious institution will not only look good but will also give you a head start in the race for the top jobs.

The best way to determine whether you need a Masters in Data Science is by grading yourself on the scorecard below. If your total adds up to more than 6 points, it would be advisable for you to earn a Master’s degree.

• You have a strong STEM (Science/Technology/Engineering/Management) background: 0 point
• You have a weak STEM background ( biochemistry/biology/ economics or another similar degree/diploma): 2 points
• You are from a non-STEM background: 5 points
• You have less than 1 year of experience in working with Python programming language: 3 points
• You have never been part of a job that requires you to code on a regular basis: 3 points
• You think you are not good at independent learning: 4 points
• You do not understand when we tell you that this scorecard is a regression algorithm: 1 point

Knowledge of programming is perhaps the most vital and fundamental skill that an aspiring data scientist must possess. Some of the other reasons why knowledge in programming is required include:

• Data sets: Data science involves working with large amounts of data sets. Knowledge of programming aids a data scientist in the analysis of large data sets.
  
  
• Statistics: The ability to program multiplies a data scientist’s ability to work with statistics. If a data scientist has knowledge about statistics but has no idea how to implement this knowledge, the knowledge of statistics becomes much less useful in his/her application of data science in his/her field of work.
  
  
• Framework: The programming ability of a data scientist also enables him/her to perform data science in a proper and efficient manner. This also enables a data scientist to build systems that an organization can make use of in order to create frameworks to automatically analyze experiments, visualize data as well as manage the data pipeline at a large organization so that the data can be accessed by the right person at the right time.

A large part of the job of a data scientist revolves around playing with data which essentially means numbers. For most of the part, these numbers are given in raw and unstructured state. The job of a data scientist is to find patterns and the relationship between them.

Below are some of the topics that you need to master in mathematics:

1. Regression
2. Linear Algebra
3. Series, sums, and inequalities
4. Real and complex numbers and their properties
5. Probability

Below are some of the topics that are must in statistics:

1. Data summaries, statistics, variance, correlations, and covariance
2. Probability distribution functions.
3. Sampling, measurements, and error
4. Constructing and testing a hypothesis.

Yes, knowledge of Structured Query Language (SQL) is required in order to become a data scientist. Data Scientists need to be able to retrieve data, in order to actually process it, analyse and make use of it. The main use of SQL for data scientists is for the retrieval of data, although some uses of data modelling and creation of a test environment may also crop up from time to time.

The job of a data scientist is not to administer or build a Hadoop cluster, but to glean useful insights from the data that is available, no matter where it comes from. Each data scientist must be able to obtain data in order to perform an analysis and Hadoop is the technology that enables the storage of large volumes of data for a data scientist to work on. So no, you do not NEED to learn Hadoop in order to become a Data Scientist, but you do need to learn some or the other tool that is similar to Hadoop.

Computer vision is used for crowd analytics, emotion analysis, verification, identification, and recognition of the image. Companies like Facebook, Instagram etc. collect image data (along with other data) from users on a daily basis. Some of the popular computer vision applications are:

• Medical Imaging: 3D imaging and image-guided surgery.
• Smart Cars: Identify objects and humans.
• Social media
• 3-D Printing and Image Capture
• Motion capture and shape capture
• Object Recognition
• Vision Biometrics

We at Knowledgehut, conduct Data Science course with Python training in all the cities across the globe and here are a few listed for your reference:

We have compiled our learning path in logical sequence to help you delve into it successfully.

1. Getting started
2. Mathematics
3. Libraries
4. Data visualization
5. Data preprocessing
6. Machine Learning and Deep Learning
7. Natural Language processing
8. Polishing skills

1. Getting started: Choose a programming language in which you are comfortable. We suggest Python or R language. Understand what data science actually means and the roles and responsibilities of a data scientist.
   
   
2. Mathematics: Data science is all about making sense of raw data, finding patterns and relationship between them and finally representing them, which is why it is crucial that you have a good command over both mathematics as well as statistics. Therefore, we have compiled some of the topics which you can pay special attention to:
   
   
1. Descriptive statistics
2. Probability
3. Linear algebra
4. Inferential statistics
3. Libraries: Data science process involves various tasks ranging from preprocessing the data given to plotting the structured data and finally to applying ML algorithms as well. Some of the famous libraries are:
   
   
1. Scikit-learn
2. SciPy
3. NumPy
4. Pandas
5. ggplot2
6. Matplotlib
   
   
4. Data visualization: It’s your job to make sense of the data given to you by finding patterns and making it as simple as possible. The most popular way to visualize data is by creating a graph. There are various libraries that can be used for this task:
   
   
1. Matplotlib - Python
2. Ggplot2 - R
   
   
5. Data preprocessing: Due to the unstructured form of data, it becomes necessary for data scientists to preprocess this data in order to make it analysis-ready. Preprocessing is done using feature engineering and variable selection. After preprocessing, our data would be in a structured form and ready to be injected into ML tool for analysis.
   
   
6. ML and Deep learning: Having deep learning skills to go along with basic ML skills on the CV is a must for every data scientist. For data analysis, deep learning is highly preferred as deep learning algorithms are designed to work when you have to deal with a huge set of data. It is recommended that you spend a few weeks on topics like neural networks, CNN, and RNN as well.
   
   
7. Natural Language processing: Every data scientist should be an expert in NLP as it involves processing of text form of data and its classification as well.
   
   
8. Polishing skills: Competitions like Kaggle etc. provide some of the best platforms to exhibit your data science skills. Apart from online competitions, you can keep on experimenting and exploring the field by creating your own projects as well.

Below are the top short courses in data science-

1. UC Berkeley Data Science for Executive
2. Artificial Intelligence Online Short Course
3. Data Analysis for Management
4. Blockchain Technologies Online Short Course
5. Oxford Fintech Programme

1. 1. UC Berkeley Data Science for Executive
      1. Offered by the Berkeley School of Information.
      2. Duration of course is 6 weeks with 8-10 hrs of study per week.
      3. Instructor is the Faculty Director, Data Scientist and Consultant of the UC Berkeley School of Information – Edward Fine
   2. Artificial Intelligence Online Short Course
      1. Offered by MIT CSAIL
      2. Duration is 6 weeks with 6-8 hrs of study per week.
      3. A personalized, people-mediated online learning experience is provided to ensure that one can grasp the subject as much as possible.
   3. Data Analysis for Management
      1. Offered by the London School of Economics and Political Science.
      2. Duration is 8 weeks with 7-10 hrs per week.
      3. Tutored by Dr. James Abdey, the Assistant Professorial Lecturer in Statistics at LSE.
   4. Blockchain Technologies Online Short Course
      1. Offered by the MIT SLOAN School of Management
      2. Duration is 6 weeks with 5-8 hrs of study per week.
      3. Learn about Blockchain, AI, Crypto economics, Digital Privacy and much more.
   5. Oxford Fintech Programme
      1. Offered by the Saïd Business School, University of Oxford.
      2. Duration is 8 weeks with 7-8 hrs of study per week.
      3. Learning with collaborative group projects.
      4. Taught by Nir Vulkan, Associate Professor of Business Economics at Oxford Saïd, along with David Shrier, Entrepreneur, futurist and Associate Fellow at Oxford Saïd.

Data science is a huge field and covering everything about data science is not possible. So it is highly advised to decide what is your area of interest in this field. There are two ways to decide what kind of data science course you want to pursue:

• Enroll yourself in data science courses to see which topics interest you and which topics are extremely difficult to understand.
  
  
• Implement your data science skills. Through thorough implementation, you can find which step of the data science phase of the project interests you more.

Data scientist has been declared as the hottest job of the 21st century. Due to high demand and less number of data scientists, data scientists earn base salaries up to 36% higher than other predictive analytics professionals. The salary of a data scientist depends on 2 things:

• Type of company
• Startups: Highest pay
• Public: Medium pay
• Governmental & Education sector: Lowest pay
• Roles and responsibilities
• Data scientist: ₹6,50,000/yr
• Data analyst: ₹4,05,000/yr
• Database Administrator: ₹6,48,987/yr

There are several career options for a data scientist –

1. Data Scientist
2. Data Architect
3. Data Administrator
4. Data Analyst
5. Business Analyst
6. Marketing Analyst
7. Data/Analytics Manager
8. Business Intelligence Manager

A Data Scientist is an individual who has the combined abilities of a mathematician, a computer scientist, and a trend spotter. The job of a Data Scientist is to decipher large volumes of data, mine the relevant parts of this data and then analyze this data so as to make predictions for similar data in the future. A career path in the field of Data Science can be explained in the following ways.

Business Intelligence Analyst: A Business Intelligence Analyst is an individual who has the job of figuring out the business as well as the market trends. This he/she does by the analysis of data in order to develop a clear picture of where exactly the business stands in the business environment.

Data Mining Engineer: A Data Mining Engineer is an individual who has the job of not only examining the data for the needs of the business, but also for the benefit of a third party. In addition to his job of the examination of data, a Data Mining Engineer also needs to create sophisticated algorithms that further aid in the analysis of data.

Data Architect: The role of a Data Architect is to work in tandem with system designers, developers and users in order to create blueprints that are used by data management systems in order to integrate, protect, maintain as well as centralize data sources.

Data Scientist: The main responsibility of a Data Scientist is to pursue a business case by analysis, development of hypotheses as well as the development of an understanding of data, so as to explore patterns from the given data. Data Scientists then move on to the development of algorithms and systems that make use of this data in a productive manner so as to further the interests of business.

Senior Data Scientist: A Senior Data Scientist is tasked with anticipating future business needs and shaping the projects, systems and data analyses of today to suit those business needs in the future.

If you are thinking to apply for a data science job, then follow the below steps to increase your chances of success:

• Study: To prepare for an interview, cover all important topics, including-
  
  
• Probability
• Statistics
• Statistical models
• Machine Learning
• Understanding of neural networks
  
  
• Meetups and conferences: Tech meetups and data science conferences are the best way to start building your network or expanding your professional connections.
  
  
• Competitions: Implement, test and keep polishing your skills by participating in online competitions like Kaggle.
  
  
• Referral: According to a recent survey, referrals are the primary source of interviews in data science companies. So, make sure your LinkedIn profile is up to date.
  
  
• Interview: If you think you are all equipped for the interviews, then go for it.Learn from the questions that you were not able to answer and study them for the next interviews.

Below are the top professional organizations for data scientists –

• Data Science Association
• International Institute for Analytics (IIA)
• International Machine Learning Society (IMLS)
• Institute for Operations Research and the Management Sciences (INFORMS)
• Association for Computing Machinery’s Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD)
• AAi: Advanced Analytics Institute
• American Statistical Association.
• SIGKDD, ACM Special Interest Group on Knowledge Discovery in Data and Data Mining
• National Center for Data Mining (NCDM)

Referrals are the most effective way to get hired. Some of the other ways to network with data scientists are:

• Data science conferences
• Online platforms such as LinkedIn and others
• Social gatherings like Meetup

Due to high demand and low supply in case of data scientists in the industry, the expectations from them are also high. However, this means that the recognition and career benefits (like salary) are exceptionally high as well. If you are aspiring to be a data scientist then we have compiled key points, which the employers generally look for in data scientists while hiring:

• Education: Most of the data scientists are Masters and PhDs in the field so it is essential that you acquire higher education if you aim to be a data scientist. Getting certified also adds to it.
  
  
• Programming: Data science is a field of computer science in general so it goes without question that your programming skills determine how well you can handle the job.
  
  
• Libraries/Tools: Programming languages are a basic platform upon which there are libraries and tools built which in turn help you in preparing, analysis, as well as visualization of data.
  
  
• Machine Learning: After preparing the data, deep learning is to be applied to it to analyze the patterns and find a relationship in it. Having ML skills is a must.
  
  
• Projects: Projects help provide proof of your skills and they help to determine your strong points and interests which in turn helps you to explore this field as well.
  
  
• Communication: Data scientist communicates not only within their own team of data scientists but with other non-tech people such as Sales team, marketing team etc. who do not understand technical language. It is, therefore, imperative that a data scientist is able to explain his/her findings in a simple way.

We have compiled the key points, which the employers generally look for while hiring data scientists:

• Education: Data scientists have more PhDs than any of the other job titles. So, getting a degree will be beneficial. Getting certified also adds to it.
  
  
• Programming: Python is a great programming language for data scientists. So, it is important to learn Python Basics before you start learning any data science libraries.
  
  
• Machine Learning: After preparing the data, deep learning is used to analyze the patterns and find a relationship. Having ML skills is a must.
  
  
• Projects: The best approach to learn data science is by practising with real-world projects so that you can build your portfolio.

There are many factors that make a program a success. Like every other educational field, the advancement in Data Science also depends on multiple reasons.

• Starting with the very basic question, are you a beginner, an intermediate learner or someone with deep prior knowledge i.e. an expert? If you’re a beginner who joins an expert program, everything will go over your head. And if you’re an expert, joining a beginner’s class would feel like a waste of time and money since you’re probably aware of whatever that’s being taught.
  
  
• Once you know what your current level is, the next question is what kind of learner are you? Whether you prefer the traditional classroom coaching where you follow a certain schedule with a specific timing or you prefer the independent style that the online coaching offers.
  
  
• Again,one of the most important factors is money and time. Since there are endless options, you must decide which one you want according to your needs.
  
  
• Always remember to check the reviews or talk to current or ex-students of the program, they will help you understand how the program can really help.
  
  
• Also, before joining a full-fledged program, make sure to try a free course. It will help you firm your decision whether you are really into data science or not.

Data Science deals with identification, representation, and extraction of meaningful information, so any programming language equipped with tools to do these tasks efficiently will be naturally popular. Python is one such popular language and the reasons for the same include:

• Short learning curve: Unlike its competitor, R, Python is comparatively easy and quick to learn due to its readable and easy-to-understand syntax.
  
  
• Scalability: YouTube migrated to Python due to its efficient scaling capabilities. As compared to its competitors - R, MATLAB etc., Python has a significant lead in scalability due to the flexibility it provides during problem-solving.
  
  
• Libraries: Python is the leading language for machine learning projects due to the packages it offers to the developers. Packages like pandas, scikit-learn, etc. allow for ML algorithms to be applied to the data easily.
  
  
• Data visualization: With the help of matplotlib, Python enables us to plot complex data representations into 2D plots. Data visualization is a significant process in the job of a data scientist. With the help of Seaborn, ggplot etc. along with matplotlib, Python provides us with a great data visualization tool.

As data science is a huge field and involves multiple libraries to work together in a smooth way, it is essential that you choose an appropriate programming language.

• R: Although it has a steep learning curve, it has various advantages.
• The big open-source community that provides R with high-quality open source packages.
• Includes loads of statistical functions and handles matrix operations smoothly.
• Via ggplot2, R provides us with a great data visualization tool.
• Python: Though it has fewer packages than R, Python is still one of the most sought after languages in the data science field.
• Pandas, scikit-learn, and tensorflow provide with most of the libraries needed for data science purposes.
• Easy to learn and implement it.
• It has a big open-source community as well.
• SQL: SQL is a structured query language which works upon relational databases.
• Pretty readable syntax.
• Efficient at updating, manipulating and querying data in relational databases.
• Java: Even though it has less number of libraries for data science purposes and with Java’s verbosity limiting its potential, it has many advantages as well:
• Compatibility. Systems are already coded in Java in the backend, and its therefore easier to integrate Java data science projects to it.
  
• It is a high-performance, general purpose, and a compiled language.
• Scala: Scala runs on JVM and has a complex syntax. Still, it is a preferred language in data science domain due to the following advantages:
• As it runs on JVM, any Scala program can run on Java as well.
• When used along with Apache Spark, we get high-performance cluster computing.

Follow these steps to successfully install Python 3 on windows:

• Download and setup: Go to the download page and setup your python on your windows via GUI installer. While installing, select the checkbox at the bottom asking you to add Python 3.x to PATH, which is your classpath and will allow you to use Python’s functionalities from terminal.

Alternatively, you can also install Python via Anaconda as well. Check if Python is installed by running the following command, you will be shown the version installed:

python --version

• Update and install setuptools and pip: Use below command to install and update 2 of most crucial libraries (3rd party):

python -m pip install -U pip

Note: You can install virtualenv to create isolated Python environments and pipenv, which is a Python dependency manager.

You can simply install Python 3 from their official website through a .dmg package, but we recommend using Homebrew to install Python as well as its dependencies. To install Python 3 on Mac OS X, just follow the below steps:

1. Install xcode: To install brew, you need Apple’s Xcode package, so start with the following command and follow through it:$ xcode-select --install
   
   
2. Install brew: Install Homebrew, a package manager for Apple, using following command:/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"Confirm if it is installed by typing: brew doctor
   
   
3. Install Python 3: To install latest version of python, use:

brew install python

1. To confirm its version, use: python --version

You should also install virtualenv, which will help you create isolated places to run different projects and may run even on different python versions.

Follow the below steps to successfully install Python 2 on your windows:

1. Download the MSI file from the official download website and go through its GUI setup.
2. Suppose you have installed Python 2.x, so windows would create a folder 

C:\Python2x    This helps in installing multiple versions of Python on your windows machine.

1. To use python command line from terminal, go to Control Panel > System > Advanced system settings > Environment variables. Add C:\Python2x; (with semicolon) to the PATH variable value and click OK.
2. Restart the command prompt and type the following to see the installed python version: Python --version

Unstructured data refers to the undefined contents of a data set that cannot be fit into structured database tables. It is basically information that is not organized in a predefined manner nor has a data model that is pre-defined. Unstructured data is generally text-heavy but may also consist of other data such as numbers, facts, figures, audio, video etc.

While unstructured data may be difficult to organize, if a company is able to tap into it in a meaningful and efficient manner, it is like digging up a bag of gold.Unstructured data can aid companies in the formation of important business decisions if a company is able to integrate this unstructured data into their information management systems and landscapes.

Pandas and NumPy are two of the most used Python libraries for data manipulation. Most of the times they are used in a single project. Although Pandas is a library build directly off from NumPy, there are some differences between both of them.