Data Science with Python Training in Fremont, United States

Get the ability to analyze data with Python using basic to advanced concepts

  • 42 hours of Instructor led Training
  • Interactive Statistical Learning with advanced Excel
  • Comprehensive Hands-on with Python
  • Covers Advanced Statistics and Predictive Modeling
  • Learn Supervised and Unsupervised Machine Learning Algorithms

Description

Rapid technological advances in Data Science have been reshaping global businesses and putting performances on overdrive. As yet, companies are able to capture only a fraction of the potential locked in data, and data scientists who are able to reimagine business models by working with Python are in great demand.

Python is one of the most popular programming languages for high level data processing, due to its simple syntax, easy readability, and easy comprehension. Python’s learning curve is low, and due to its many data structures, classes, nested functions and iterators, besides the extensive libraries, this language is the first choice of data scientists for analysing, extracting information and making informed business decisions through big data.

This Data science for Python programming course is an umbrella course covering major Data Science concepts like exploratory data analysis, statistics fundamentals, hypothesis testing, regression classification modeling techniques and machine learning algorithms.Extensive hands-on labs and an interview prep will help you land lucrative jobs.

What You Will Learn

Prerequisites

There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.

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Who should Attend?

  • Those Interested in the field of data science
  • Those looking for a more robust, structured Python learning program
  • Those wanting to use Python for effective analysis of large datasets
  • Software or Data Engineers interested in quantitative analysis with Python
  • Data Analysts, Economists or Researchers

KnowledgeHut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.

Curriculum

Learning Objectives:

Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in  data science.

Topics Covered:

  • What is Data Science?
  • Analytics Landscape
  • Life Cycle of a Data Science Project
  • Data Science Tools & Technologies

Hands-on:  No hands-on

Learning Objectives:

In this module you will learn how to install Python distribution - Anaconda,  basic data types, strings & regular expressions, data structures and loops and control statements that are used in Python. You will write user-defined functions in Python and learn about Lambda function and the object oriented way of writing classes & objects. Also learn how to import datasets into Python, how to write output into files from Python, manipulate & analyze data using Pandas library and generate insights from your data. You will learn to use various magnificent libraries in Python like Matplotlib, Seaborn & ggplot for data visualization and also have a hands-on session on a real-life case study.

Topics Covered:

  • Python Basics
  • Data Structures in Python
  • Control & Loop Statements in Python
  • Functions & Classes in Python
  • Working with Data
  • Analyze Data using Pandas
  • Visualize Data 
  • Case Study

Hands-on:

  • Know how to install Python distribution like Anaconda and other libraries.
  • Write python code for defining your own functions,and also learn to write object oriented way of writing classes and objects. 
  • Write python code to import dataset into python notebook.
  • Write Python code to implement Data Manipulation, Preparation & Exploratory Data Analysis in a dataset.

Learning Objectives: 

Visit basics like mean (expected value), median and mode. Understand distribution of data in terms of variance, standard deviation and interquartile range and the basic summaries about data and measures. Learn about simple graphics analysis, the basics of probability with daily life examples along with marginal probability and its importance with respective to data science. Also learn Baye's theorem and conditional probability and the alternate and null hypothesis, Type1 error, Type2 error, power of the test, p-value.

Topics Covered:

  • Measures of Central Tendency
  • Measures of Dispersion
  • Descriptive Statistics
  • Probability Basics
  • Marginal Probability
  • Bayes Theorem
  • Probability Distributions
  • Hypothesis Testing 

Hands-on:

Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario

Learning Objectives: 

In this module you will learn analysis of Variance and its practical use, Linear Regression with Ordinary Least Square Estimate to predict a continuous variable along with model building, evaluating model parameters, and measuring performance metrics on Test and Validation set. Further it covers enhancing model performance by means of various steps like feature engineering & regularization.

You will be introduced to a real Life Case Study with Linear Regression. You will learn the Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. It also covers techniques to find the optimum number of components/factors using screen plot, one-eigenvalue criterion and a real-Life case study with PCA & FA.

Topics Covered:

  • ANOVA
  • Linear Regression (OLS)
  • Case Study: Linear Regression
  • Principal Component Analysis
  • Factor Analysis
  • Case Study: PCA/FA

Hands-on: 

  • With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling.

Learning Objectives: 

Learn Binomial Logistic Regression for Binomial Classification Problems. Covers evaluation of model parameters, model performance using various metrics like sensitivity, specificity, precision, recall, ROC Cuve, AUC, KS-Statistics, Kappa Value. Understand Binomial Logistic Regression with a real life case Study.

Learn about KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K. Understand KNN through a real life case study. Understand Decision Trees - for both regression & classification problem. Understand Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID. Use a real Life Case Study to understand Decision Tree.

Topics Covered:

  • Logistic Regression
  • Case Study: Logistic Regression
  • K-Nearest Neighbor Algorithm
  • Case Study: K-Nearest Neighbor Algorithm
  • Decision Tree
  • Case Study: Decision Tree

Hands-on: 

  • With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to default a credit card payment next month. This can help the bank be proactive in collecting dues.
  • Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
  • Wine comes in various types. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Learning Objectives:

Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
Work on a real- life Case Study with ARIMA.

Topics Covered:

  • Understand Time Series Data
  • Visualizing Time Series Components
  • Exponential Smoothing
  • Holt's Model
  • Holt-Winter's Model
  • ARIMA
  • Case Study: Time Series Modeling on Stock Price

Hands-on:  

  • Write python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
  • Write python code to Use Holt's model when your data has Constant Data, Trend Data and Seasonal Data. How to select the right smoothing constants.
  • Write Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Dataset including features such as symbol, date, close, adj_close, volume of a stock. This data will exhibit characteristics of a time series data. We will use ARIMA to predict the stock prices.

Learning Objectives:

A mentor guided, real-life group project. You will go about it the same way you would execute a data science project in any business problem.

Topics Covered:

  • Industry relevant capstone project under experienced industry-expert mentor

Hands-on:

 Project to be selected by candidates.

Meet your instructors

Become an Instructor
Sukesh

Sukesh Marla

Founder

Irrespective of project size I believe in working as a team. We are a team of highly qualified engineers with each specializing in their own field like designing, testing and development.
Working in a team ensures the work is not affected in case of any eventuality of any of team member. This guarantees timely delivery.

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Biswanath

Biswanath Banerjee

Trainer

Provide Corporate training on Big Data and Data Science with Python, Machine Learning and Artificial Intelligence (AI) for International and India based Corporates.
Consultant for Spark projects and Machine Learning projects for several clients

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Projects

Predict House Price using Linear Regression

With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.

Predict credit card defaulter using Logistic Regression

This project involves building a classification model.

Read More

Predict chronic kidney disease using KNN

Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.

Predict quality of Wine using Decision Tree

Wine comes in various styles. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Note:These were the projects undertaken by students from previous batches. 

Data Science with Python

What is Data Science

In 2012, the Harvard Review named Data Science as the sexiest job of the 21st century.  What makes data science such a hot topic?  The answer is simple, data. Data has become an integral part of our lives and it has become difficult to ignore its potential. In a city like Fremont, CA, there are several corporations that are looking for data scientists to help them harness the potential of data including Tailored Brands, Softsol, Astreya, WAY, Snapwiz, Facebook, Trabajo, SLD Laser, pony.ai, Soraa, Inceptio Technology, SSIT, Central Business Solutions, Quest Groups, Lam Research Corporation, etc. From classifying target audience to improving customer experience across devices and channels, data science offers immense value to businesses.

The top technical skills required to become a data scientist in Fremont, CA, USA, include the following:

  1. Python Coding: One of the most popular languages used in Data Science, Python aids in preprocessing of data and taking different formats of data as input. Its versatility and simplicity gives it an advantage over other programming languages.
  2. R Programming: R programming is another popular language used in the field of data science. It is used as an analytical tool used for solving statistical problems.
  3. Hadoop Platform: Although not a must, knowledge of the Hadoop platform is important for every data scientist. There are several projects in which Hadoop is used. 
  4. SQL database and coding: SQL is used by data scientists for working with data. This involves accessing and communicating with the data. The data scientist must have complete insights about the database they are working on.
  5. Machine Learning and Artificial Intelligence: Machine Learning skills are a must to even be considered for the post of a data scientist. You must be aware of the field of Artificial Intelligence. Make sure that you have an in-depth knowledge of topics like Decision trees, neural networks, adversarial learning, reinforcement learning, logistic regression, etc.
  6. Apache Spark: Like Hadoop, Apache Spark is a data-sharing technology used for performing big data computation. However, it is much faster than Hadoop because unlike Hadoop that reads and writes to the disk, Spark uses the system’s memory to cache its computation.
  7. Data Visualization: This is one of the most important skills a data scientist must have. A data scientist must be able to present the analyzed data in a form that can be understood by the non-technical members of the team. For this, visualization tools like matplotlib, tableau, d3.js, ggplot, etc. are used.

To become a successful data scientist, one must have the following behavioral traits:

  • Curiosity – Since a data scientist has to deal with a lot of data, it is easy to lose interest. So, one must have a hunger for knowledge to make it in the field of data science.
  • Clarity – You must look for clarity at all times. Whether you are cleaning up data or writing code, you need to be clear why you are doing it, what you are doing, and how you are doing it. 
  • Creativity – To look beyond the obvious and find innovative solutions, a data scientist must be creative as well. You must be able to create new innovative tools for analyzing, developing modeling features, and visualize the data.
  • Skepticism – Although creativity is an essential skill, a data scientist must also be skeptical to create a line between a data scientist and a creative mind. They must stay in the real world and not get carried away with creativity.

If you are not sure whether you should study data science or not, here are 5 benefits of becoming a data scientist that will help you decide:

  1. High Pay: Data Scientist Jobs are one of the highest paying jobs in the IT industry today. The average salary for a Data Scientist is $111,748 per year in Fremont, CA.
  2. Good bonuses: When you get hired as a data scientist in a company, you will be able to enjoy signing bonus, equity shares, and bonuses.
  3. Education: A Master's degree or Ph.D. is required to get a job as a data scientist. Once you have that, you can also apply to become a lecturer or researcher in a government or private institution.
  4. Mobility: Data Scientists are in great demand in developed countries. So, getting a job in such a country will not only offer you a handsome salary but also an improved standard of living.
  5. Network: There are several conferences, meetups, and tech talks organized for data scientists that you can use for building your professional network. You will need this in the future for referral purposes.

Data Scientist Skills and Qualifications

If you want to become a top-notch data scientist, you need to have these 4 business skills:

  1. Analytic Problem-Solving – The first thing you do when you have a problem is to analyze it. You need to have a clear understanding and perspective of what the problem demands and work towards a solution accordingly.
  2. Communication Skills – Data Scientists require the right communications skills to bridge the gap between the technical and non-technical members of the team. They need to communicate customer analytics and deep business effectively.
  3. Intellectual Curiosity – A data scientist must always be curious to ask questions like ‘why’ or ‘how’ until you get the right answer.
  4. Industry Knowledge – A data scientist must have a strong knowledge of the industry they are working in. This will help them in cleaning up the data as they will know what needs to be ignored.

If you are thinking of going for an interview to get a job in the field of data science, here are the 5 best ways to brush up your data science skills:

  • Boot camps: Boot camps are the perfect way to get theoretical as well as practical knowledge of data science. They last for about 4-5 days and will help you brush up your data science skills in no time.
  • MOOC courses: These are the online courses where experts from the field of data science will help you brush up your data science skills by giving you assignments to work on your implementation skills. You will also get acquainted with the latest trends in the field of data science.
  • Certifications: Getting certified in a data science course will not only help you work on your skills but also improve your CV. Here are a few data science certifications that you must look into:
    • Cloudera Certified Associate - Data Analyst
    • Cloudera Certified Professional: CCP Data Engineer
    • Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
  • Projects: Work on some projects. These are the best way to brush up on your data science skills as you will be able to work on your implementation skills. You can either explore different solutions to old projects or work on new projects.
  • Competitions: Lastly, you can participate in online competitions that will help you work on your problem-solving skills. Kaggle is one such competition.

There are several companies that have understood the potential of data science and are actively looking for data scientists in Fremont, CA, including Tailored Brands, Softsol, Astreya, WAY, Snapwiz, Facebook, Trabajo, SLD Laser, pony.ai, Soraa, Inceptio Technology, SSIT, Central Business Solutions, Quest Groups, Lam Research Corporation, Harnham, Tesla, Seagate Technology, Sleep Number, Tokyo Electron America, Ivy Exec, etc. to help them optimize their business processes.

To practice your data science skills with data sets, you can select one of the following problems which are categorized based on your expertise and their difficulty level:

  • Beginner Level
    • Iris Data Set: This is a perfect dataset for a beginner. Containing just 4 columns and 50 rows, the Iris dataset will help you learn about pattern recognition and classification techniques.Practice Problem: Predicting the flower’s class with the help of given parameters.
    • Loan Prediction Data Set: If you want to learn how banking and insurance domain work, this dataset will work for you. It has 13 columns and 615 rows that use data analytics and data science methodologies. This is a classification problem.Practice Problem: Predicting whether the given loan will be approved or not.
  • Intermediate Level:
    • Black Friday Data Set: Containing 12 columns and 550,069 rows, it is a regression problem that consists of daily transactions of millions of customers in a retail store. This dataset will be very effective in helping you explore your engineering skills.
      Practice Problem: Predicting the amount of total purchase.
    • Text Mining Data Set: This dataset contains 30,438 rows and 21,219 columns. It is a high dimensional, multi-classification problem that contains safety reports of problems caused during the flights. It was collected in 2007 in the Siam Text Mining competition.
      Practice Problem: Classifying the documents using the labels.
  • Advanced Level:
    • Identify the digits data set: This dataset contains 7000 images of 82X28 dimensions each. The elements present in the image are studied, analyzed and recognized.
      Practice Problem: Identifying the different elements present in the image.
    • Vox Celebrity Data Set: This dataset is for you if you want to work with audio processing. It contains 1000,000 words, spoken by 1,251 celebrities. Extracted through YouTube videos.
      Practice Problem: Identifying the voice of the celebrity.

How to Become a Data Scientist in Fremont, California

If you want to become a top-notch data scientist, you need to follow the below-mentioned steps:

  1. Getting started: The first step is to select a programming language that you are familiar with. Python and R are the most popular and preferred programming languages used in the field of data science.
  2. Mathematics and statistics: Most of the data that is generated in an unstructured form. This can be text, numbers, images, audios, videos, etc. To decipher a pattern in this data, knowledge of mathematics and statistics is required.
  3. Data visualization: When you are working in a team, you will also have non-technical members on the team. To help them understand the data and get them on the same page, you need to be able to visualize the data. You can use graphs and charts for the same.
  4. ML and Deep Learning: Deep learning and machine learning skills are a must for becoming a data scientist. It is required to build tools used for analyzing the data.

Here are some key steps and skills that are a must if you want to become a successful data scientist:

  1. Degree/certificate: Getting a degree in data science will help you jumpstart your career. You can go for an online or an offline course, depending on what suits your needs the best. During the course, you will learn the fundamentals of data science and how to use the latest tools.
  2. Unstructured data: As a data scientist, you must be able to process unstructured data. Most of the data that we have is in unstructured form, i.e., unorganized and unlabelled. It is not easy to fit this data into a database and requires several complex procedures. You will be able to manipulate the data only after this unstructured data has been processed.
  3. Software and Frameworks: While working in the field of Data Science, you will be required to work with different software, frameworks, and programming languages. Python and R are some of the languages used in data science. You must also be an expert in a framework like Hadoop and Apache Spark. Last but not least, you must know SQL to work with the database.
  4. Machine learning and Deep Learning: Without machine learning and deep learning skills, you won’t be able to get a job as a data scientist. You will need this after the collection and preparation of data, to apply algorithms and analyze the data. To train the model, you will need deep learning skills.
  5. Data visualization: A data scientist is also responsible for helping in data visualization. This is used to help others understand the data as well and make crucial marketing decisions. Tools that can be used for data visualization include matplotlib, ggplot2, etc.

A degree in data science can help you get a job and jumpstart your career. Here is how::

  • Networking – During your course, you will be able to make friends that will help you build your professional network. This can help you get a referral and land a job in the future.
  • Structured learning – If you have trouble in self-learning, getting enrolled in a course will help in structured learning. This is because you will have to follow a strict schedule and study according to the curriculum.
  • Internships – An in-office internship is part of a degree. During the internship, you will get the much-required hands-on experience.
  • Recognized academic qualifications for your résumé – A degree from a reputed institution will put your CV above others and help you land a better job in the data science field.

If you are having trouble deciding if you should get a Master's degree in Data Science or not, here is a scorecard that will help you do so. If your total adds up to more than 6 points, a degree is advised:

  • Strong STEM (Science/Technology/Engineering/Management) background: 0 point
  • Weak STEM background (biochemistry/biology/economics or another similar degree/diploma): 2 points
  • Non-STEM background: 5 points
  • < 1 year of experience in Python: 3 points
  • 0 year of experience in regular coding for a job: 3 points
  • Not good at independent learning: 4 points
  • Don’t understand that this scorecard is a regression algorithm: 1 point

Programming language is one of the most important requirements to become a data scientist. Here are the reasons explaining why:

  • Data sets: You will be working with big datasets in data science. Knowledge of programming language is required for analyzing these datasets.
  • Statistics: Statistics is required for analyzing the data, deciphering patterns, and finding relationships in the data. However, just the knowledge of statistics is not enough. You need to have programming skills to implement this knowledge; otherwise, it is of no use.
  • Framework: Programming skills will be required for building a system that will be used for creating frameworks. These will be then used for automating the analysis of experiments, management of data pipeline, and visualization of the data.

Data Scientist Jobs in Fremont, California

If you want to get a job as a data scientist, you need to follow the below-mentioned learning path:

  1. Getting started: The first step is to select the programming language you will be working in. You can choose any language used in data science. However, Python and R are recommended since they are the most popular. You must also be aware of what data science means and what are the roles and responsibilities of a data scientist.
  2. Mathematics: Good command in statistics and mathematics is required to collect the data, decipher patterns, find relationships, and visualize the data. The topics that you must focus on are linear algebra, probability, inferential statistics, and descriptive statistics.
  3. Libraries: Some certain libraries and packages are used for data preprocessing, plotting of structured data, and then use machine learning algorithms to analyze the data. These include, Matplotlib, ggplot2, Pandas, NumPy, SciPy, Scikit-learn, etc. 
  4. Data visualization: Once the data is analyzed, it is the job of a data scientist to visualize it in such a way that is easily understandable for everyone to understand. The most popular ways include charts and graphs. You can use any of the following tools for data visualization:
    • Ggplot2 - R
    • Matplotlib - Python
  5. Data preprocessing: Over 2.5 quintillion bytes of data are created every single day. This data is unlabelled and unorganized. This unstructured data requires preprocessing before it can be injected into the ML tool for analysis. Feature engineering and variable selection are some of the processes required to do this job.
  6. ML and Deep learning: You need to have deep learning and machine learning skills for analyzing the large volumes of the dataset. CNN, RNN, and Neural networks are some of the topics you must focus on.
  7. Natural Language processing: To process and classify the data present in a textual form, you must know about natural language processing. 
  8. Polishing skills: Lastly, you can polish your skills by participating in an online competition like Kaggle. You can also try working on old projects or creating new ones.

If you are preparing for a job as a data scientist, here are 5 important steps that you must follow:

  • Study: Brush up on all the important topics before the interview including the following:
    • Probability
    • Statistics
    • Statistical models
    • Machine Learning
    • Understanding neural networks
  • Meetups and conferences: Start attending data science conferences, tech talks, and meetups to meet other data scientists and build your professional network. This will help you with referrals.
  • Competitions: For testing, implementing and polishing your data science skills, participate in online competitions like Kaggle.
  • Referral: Maintain your LinkedIn profile. This will help you with the referrals, which is the primary source of interviews in the IT sector.
  • Interview: Go for the interview. Doesn’t matter if you can’t get a few questions right. Make sure that you study them and prepare better for the next one.

The job of a data scientist is complex. There are several roles and responsibilities of a data scientist including the following:

  • Collect the data that is required for the analysis.
  • Convert this mostly unstructured data into a structured form.
  • Use machine learning tools, programs, and techniques to make sense of this data.
  • Deliver insights and predict future outcomes after performing statistical analysis on the data.

The average salary for a Data Scientist is $111,748 per year in Fremont, CA

https://www.indeed.com/salaries/Data-Scientist-Salaries,-Fremont-CA 

A data scientist is a part mathematician, computer scientist, and part trend spotter. Here is how the career path of a data scientist goes:

  • Business Intelligence Analyst: The job of a business intelligence analyst is to determine the working of the business and how market trends affect it. They analyze the data to get the exact picture of the current standing of the business.
  • Data Mining Engineer: A data mining engineer’s job is examining the data required by the business. They also create algorithms required for data analysis. Many companies hire them as a third party.
  • Data Architect: A Data Architect works alongside system designers, developers, and users to create blueprints required for data sources’ integration, centralization, maintenance, and protection.
  • Data Scientist: It is the job of a data scientist to analyze the data, develop a hypothesis, and explore the patterns in the given data. They create algorithms and develop data capable of converting raw data into meaningful insights. 
  • Senior Data Scientist: A senior data scientist anticipates the needs of the business in the future. He/she makes sure that these needs are kept in mind while shaping the systems, data analysis process, and all the projects.

To network with other data scientists, you can try any of the following:

  • Social gatherings like Meetup 
  • An online platform like LinkedIn
  • Data science conference

The top 8 data science career opportunities in 2019 are – 

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

To get employed as a data scientist, you need to have mastery of the following tools and software:

  • Education: You need to either have a Master's degree or a Ph.D. in Data Science to get a job as a data scientist. Getting certifications will also improve your CV.
  • Programming: It is perhaps the most important skill required to be a data scientist. Start with the basics of a programming language and then move on to data science libraries.
  • Machine Learning: Deep learning and machine learning skills are required to become a data scientist. You need to be able to analyze the data and find relationships.
  • Projects: Work on a couple of real-world projects. This will not only improve your implementation skills but also improve your portfolio.

Data Science with Python Fremont, California

Python is considered to be the most preferred and popular language in the field of data science. It is a structured and object-oriented language that offers several libraries and packages that help while working in data science projects. It is simple and easy to learn, read, and understand. There are also several resources available that will help you learn the language and find a way out whenever you are stuck in a problem. 

The 5 most popular programming languages used in the field of data science include:

  • R: Even though it is difficult to learn, it is one of the most popular languages in data science. R has a big, open-source community that offers high-quality, open-source packages. 
  • Python: It is the most popular language used in the field of data science due to the following:
    • Easy to learn, read, understand and implement
    • A big, open-source community
    • Comes with libraries like pandas, scikit-learn, and tensorflow that are used in data science
  • SQL: It is required for dealing with relational databases. It has the following characteristics:
    • The syntax is easy to read, write, and understand
    • Efficient in querying, manipulating, and updating databases
  • Java: The verbosity and a limited number of libraries make Java difficult to use in data science. However, it still offers the following advantages:
    • Since there are already so many systems coded in java, it makes integrating the data science project easier.
    • It is a high-performance, general-purpose, and compiled language.
  • Scala: Even though it has a complex syntax, the following reasons make Scala a preferred language in the data science field:
    • It is compatible with Java as it runs on JVM
    • When used along with Apache Spark, it can perform cluster computing

To download and install Python 3 on Windows, you need to follow these steps:

  • Download and setup: Go to the download page and use a GUI installer to install python on Windows. Check the box that asks to add Python 3.x to PATH allowing the python’s functionalities to work from the terminal.

  • Use the following command to check the python’s version installed on your system:

python --version

  • Update and install setuptools and pip: For installing and updating crucial libraries (3rd party), use the following:

python -m pip install -U pip

To install Python 3 on Mac OS X, follow these steps:

  • Install Xcode: Install the Xcode package of Apple by using the following command: 

$ Xcode-select --install

brew install python

  • Confirm the python’s version installed in the computer using: 

python --version

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I would like to extend my appreciation for the support given throughout the training. My trainer was very knowledgeable and I liked the way of teaching. The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut.

Ike Cabilio

Web Developer.
Attended Certified ScrumMaster (CSM)® workshop in May 2018
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My special thanks to the trainer for his dedication, learned many things from him. I liked the way they supported me until I get certified. I would like to extend my appreciation for the support given throughout the training.

Prisca Bock

Cloud Consultant
Attended Certified ScrumMaster (CSM)® workshop in May 2018
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The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut. I really liked the way the trainer explained the concepts. He is very patient.

Anabel Bavaro

Senior Engineer
Attended Certified ScrumMaster (CSM)® workshop in May 2018

FAQs

The Course

Python is a rapidly growing high-level programming language which enables clear programs on small and large scales. Its advantage over other programming languages such as R is in its smooth learning curve, easy readability and easy to understand syntax. With the right training Python can be mastered quick enough and in this age where there is a need to extract relevant information from tons of Big Data, learning to use Python for data extraction is a great career choice.

 Our course will introduce you to all the fundamentals of Python and on course completion you will know how to use it competently for data research and analysis. Payscale.com puts the median salary for a data scientist with Python skills at close to $100,000; a figure that is sure to grow in leaps and bounds in the next few years as demand for Python experts continues to rise.

  • Get advanced knowledge of data science and how to use them in real life business
  • Understand the statistics and probability of Data science
  • Get an understanding of data collection, data mining and machine learning
  • Learn tools like Python

By the end of this course, you would have gained knowledge on the use of data science techniques and the Python language to build applications on data statistics. This will help you land jobs as a data analyst.

Tools and Technologies used for this course are

  • Python
  • MS Excel

There are no restrictions but participants would benefit if they have basic programming knowledge and familiarity with statistics.

On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.

Your instructors are Python and data science experts who have years of industry experience. 

Finance Related

Any registration canceled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of a written request for refund. Kindly go through our Refund Policy for more details.

KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

The Remote Experience

In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor

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