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Data Science with Python Training in Chennai, India

Learn to analyze data with Python in this Data Science with Python comprehensive course.

  • 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

Online Classroom (Weekend)

Apr 04 - May 09 10:00 AM - 02:00 PM ( IST )

INR 42999

INR 25999

Online Classroom (Weekend)

Apr 04 - May 09 12:30 PM - 04:30 PM ( IST )

INR 42999

INR 25999

CITREP+ funding support is eligible for Singapore Citizens and Permanent Residents


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 analyzing, 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 interview prep will help you land lucrative jobs.

What You Will Learn


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

3 Months FREE Access to all our E-learning courses when you buy any course with us

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.


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


  • 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 


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:

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


  • 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


  • 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
  • Case Study: Time Series Modeling on Stock Price


  • 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


 Project to be selected by candidates.


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, Data Scientist was named as the ‘Sexiest Job of the 21st century’ by the Harvard Business Review. The reason behind this popularity is data. Over 2.5 quintillion bytes of data are created every single day and companies are continuously figuring out a way to make the best use of this data. Chennai is one of the metro cities of India and is home to several leading companies, including Crayon Data, Shell, FIS, Hinduja Tech, Ideas2IT, tvs, eHelium, Mindtree, etc. All these companies are looking for expert data scientists to help them make decisions on product and operating metrics.

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

  1. Python Coding: This is the most important skills required to become a data scientist. It helps in the preprocessing of data and can take various data formats. It is also simple and versatile and helps in creating and performing dataset operations.
  2. R Programming: Knowledge of R programming is required for making data science problems easier to solve.
  3. Hadoop Platform: Although not a must, knowledge of Hadoop is required as several projects use this framework for data analysis.
  4. SQL database and coding: Knowledge of SQL, or Structured Query Language, is required for dealing with databases that include accessing, communicating, and working with data.
  5. Machine Learning and Artificial Intelligence: Proficiency in Machine Learning and Artificial Intelligence is a must to become a data scientist. Make sure that you cover topics like a neural network, logistic regression, decision trees, adversarial learning, reinforcement learning, machine learning algorithms, etc. 
  6. Apache Spark: It is a data-sharing technology used for computation. It uses the system's memory to cache its computation, unlike Hadoop that reads and writes to the disk.
  7. Data Visualization: A data scientist is required to present the analyzed data in a form that is easy to understand. Tools like matplotlib, d3.js, ggplot, and Tableau are used for converting the complex results into a format that can be easily understood.
  8. Unstructured data: A data scientist has to deal with unstructured data most of the time. They need to organize this data and convert it into a structured form. This data includes social media posts, customer reviews, blogs, audio, video samples, etc.

If you want to become a successful Data Science professional, you need to incorporate the following behavioral traits:

  • Curiosity – A data scientist must be curious enough to ask questions like ‘why’, ‘what’, and ‘how’.
  • Clarity – With so much data comes so much complexity. You must be able to make sense of this data and provides insights. This requires clarity.

  • Creativity – Creativity will help a data scientist create new tools for analysis and develop new features. Also, one needs creativity for visualizing data.
  • Skepticism – Along with creativity, a data scientist must also be skeptical so that he/she stays in the real world and is not carried too far with creativity.

The 5 proven benefits of being a Data Scientist include:

  1. High Pay: Since there are not enough trained and experienced data scientists, they are in high demand. As a result, they are being handsomely paid.
  2. Good bonuses: Data scientists can also expect perks, such as a signing bonus, equity shares, and stock incentive bonuses.
  3. Education: A Master's degree or Ph.D. is required to get a job as a data scientist. As a result, you can try for a job as a researcher or a lecturer in a private or government institute.
  4. Mobility: There are many companies in developed countries hiring data scientists. This results in a hefty salary and an improved living standard.
  5. Network: There are several tech talks, conferences, and meetups organized for data scientists where you can network with fellow professionals.

Data Scientist Skills and Qualifications

The 4 must-have business skills required to become a data scientist:

  1. Analytic Problem-Solving – As a data scientist, you must have analytical problem-solving skills. It will help understand the problem before you look for a solution and develop your strategy accordingly.
  2. Communication Skills – It is the job of a data scientist to communicate customer analytics and deep business. 
  3. Intellectual Curiosity – A data scientist constantly has to ask questions like ‘why' and ‘how' to deliver the best results to the organization.
  4. Industry Knowledge – Knowledge of the industry that you are working in is required to make sure that you know what is relevant and what is not.

If you are looking for a job as a data scientist and want to brush up your data science skills, here is what you need to do:

  • Boot camps: Boot camps last for 4-5 days and are a perfect way to get theoretical knowledge and hands-on experience in data science.
  • MOOC courses: Taught by data science experts, these online courses provide assignments that will help you brush up your data science skills and keep you updated with the latest technology trends in the field of data science.
  • Certifications: Getting certified will not only improve your skills but also your CV. Here are a few certifications in data science that you can go for:
    • Cloudera Certified Associate - Data Analyst
    • Cloudera Certified Professional: CCP Data Engineer
    • Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
  • Projects: Projects are the best way to brush up your skills. You can try creating new projects or work on old projects.
  • Competitions: Practicing in online competitions like Kaggle will not only improve your data science skills but also your problem-solving skills.

We live in a world of data. Your investment in the stock market is data, your medical diagnosis is data, your browsing history is data and the list goes on. Most companies in Chennai collect data for their benefit. Leading companies in Chennai such as Crayon Data, Shell, FIS, Hinduja Tech, Ideas2IT, tvs, eHelium, Mindtree, etc. are looking for expert data scientists to improve product performance, building prediction models, affinity maps, and cluster analysis, etc.

To practice your data science skills, you can try one of the following datasets that are categorized according to their difficulty level:

  • Beginner Level
    • Iris Data Set: This dataset has 4 columns and 50 rows. It is one of the easiest, versatile and resourceful dataset that is perfect for beginners. It is used for pattern recognition using classification techniques.
      Practice Problem: Predicts the flower’s class using the given parameters.
    • Bigmart Sales Data Set: This dataset is a regression problem consisting of 12 variables and 85,223 rows. While working with this dataset, you will get introduced to concepts like inventory management, customizations, product bundling, etc. 
      Practice Problem: Predict the store’s total sales.
  • Intermediate Level:
    • Black Friday Data Set: It is a regression problem consisting of 12 columns and 550,069 rows. With this dataset, you will be able to understand how millions of customers shop every day.
      Practice Problem: Predict the total purchase amount.
    • Human Activity Recognition Data Set: This dataset was collected from 30 people where inertial sensors were used for recording. It has 561 columns and 10,299 rows.
      Practice Problem: Predict the human activity’s category.
  • Advanced Level:
    • Identify the digits data set: With 7000 images of 82X28 dimensions each, this dataset is used to study, analyze and recognize the elements present in the image.
      Practice Problem: Identify the elements present in the image.
    • Vox Celebrity Data Set: This dataset contains 100,000 words spoken by 1,251 celebrities. Extracted from YouTube videos, this dataset isolates speech for audio processing.
      Practice Problem: Identify the celebrity’s voice.

How to Become a Data Scientist in Chennai, India

Follow the below-mentioned steps to become a successful data scientist:

  1. Getting started: Select the programming language you are comfortable working in. You can try Python and R as they are the most sought after programming languages used in the field of Data Science.
  2. Mathematics and statistics: Knowledge of mathematics and statistics are required for analyzing the data, deciphering patterns in it, and figuring out the relationship among them.
  3. Data visualization: Data visualization is required so that even the non-technical members of the team and the users can understand the data. 
  4. ML and Deep learning: These are required for creating the systems and tools needed for analyzing the data.

Here are a series of steps you need to follow to become a data scientist:

  1. Degree/certificate: It is the first step towards getting a job as a data scientist. It will not only improve your CV but also help you build your professional network and have a thorough understanding of the basics of data science and the latest tools used in the field of data science.
  2. Unstructured data: One of the most important jobs of a data scientist is to analyze the data. But before it can be analyzed, the data must be converted into a structured form. Only after this, the manipulation of data is possible.
  3. Software and Frameworks: While working in data science, you will have to deal with unstructured data. This is where software and frameworks come in handy. Also, you must be skilled in programming to implement your data science skills.
    1. R is a complex language that is one of the most used languages in the field of data science. It helps in dealing with statistical problems.
    2. Hadoop framework is used when the amount of data available for processing is higher than the available memory. It uses different parts of the machine to convey the data. Spark is also a framework that is faster than Hadoop as it uses the system memory to cache its computation. It also prevents data loss.
    3. You also need to have an understanding of SQL and databases.
  4. Machine learning and Deep Learning: Machine learning and deep learning skills are required to analyze the data that was collected and prepared. Deep learning is used for training the model used for data analysis.
  5. Data visualization: It is used by data scientists to present the data in a form that is simple and understandable. It also helps in making informed decisions. Tools like matplotlib and ggplot2 are used for this purpose.

Getting a degree in Data Science from a reputed institution can help you get ahead in your career. The advantages of getting a degree in Data Science include:

  • Networking – When you are in a college, you will be able to build your network by making friends and acquaintances from the same industry.
  • Structured learning – If you are one of the people who are not good at self-learning, a degree will help you keep focused as you will have to follow a schedule to keep up with the curriculum.
  • Internships – An internship will help you get the much necessary practical, hands-on experience.

  • Recognized academic qualifications for your résumé – A degree from a prestigious institution is sure going to improve your CV.

The below-mentioned scorecard will help you determine if you need a Master's degree or not. If you get a score of more than 6 points, a Master's degree in Data Science is required.

  • 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 knowledge is the most basic and essential skill required to get a job in the field of data science. Here is why:

  • Data sets: To analyze the large datasets, one must know a programming language.
  • Statistics: For deciphering patterns and relationships in the data, you need to know statistics. And to implement this knowledge, you need to have programming skills.

  • Framework: With the help of a programming language, you will be able to create a system or a framework that will help satisfy the needs of the organization. This includes automatically analyzing the experiments, visualizing the data, and managing the data pipeline.

Data Scientist Salary in Chennai, India

In Chennai, a Data Scientist earns a pay of Rs. 8,19,815.

Data Scientist working in Chennai earn an average of about Rs. 8,19,815 as compared to Rs. 5,89,851 in Pune.

The earning of a Data Scientist is Rs. 8,19,815 per year as compared to Rs. 6,13,889 earned by a Data Scientist working in Hyderabad.

The annual earnings of a Data Scientist in Chennai is Rs. 8,19,815 as compared to Rs. 6,15,496 in Bangalore.

Data Scientist in Chennai earns about Rs. 8,19,815 every year. Data Scientists working in Coimbatore earn Rs. 3,60,000 per year.

The average annual salary of a Data Scientist in Chennai is about Rs. 8,19,815 every year as opposed to Data Scientists working in Madurai who earn Rs. 13,05,000 per year.

Many organizations in Chennai are looking for data scientists. There are several job listings in various portals offering handsome salaries to data scientists. So, it is clear that the demand for Data Scientists in Chennai is high.

If you are a Data Scientist in Chennai, you can stay assured that the city has more to offer than just the beaches and the beautiful weather. There are a number of companies that are looking to hire data scientists. This means, there will be plenty of job opportunities for you to display your expertise in data science.

In Chennai, there are several advantages of being a Data Scientist apart from the salary. There are several firms in the city that are searching for data scientists who can leverage data and help the business grow. This offers data scientists many opportunities for tremendous job growth in this city. The presence of several high tech companies in the city has also enhanced the playing field for data scientists who can explore their options in a variety of sectors from technology to pharma to government positions that use data science to meet business objectives.

If you are in Chennai and looking for a Data Scientist job, you can apply at Wipro Ltd, Wabco, Avira Operations GmbH & Co. KG, Ford Global Business Services, Vestas, Ericsson and many more.

Data Science Conference in Chennai, India

S.NoConference nameDateVenue
1.2nd National Conference on Data Science and Intelligent Information Technology NCDSIIT 186-7th April, 2018
Rajalakshmi Institute of Technology, Kuthambakkam, Chennai
2.Artificial Intelligence Summit, Chennai, India
11 May, 2019

Seminar Hall, Cresent Innovation & Incubation Council B S A Cresent Institute of Science and Technology, Seethakathi Estate, GST Road,Vandalur, Chennai-600048.

1. 2nd National Conference on Data Science and Intelligent Information Technology NCDSIIT 18, Chennai

  • Conference City: Chennai, India 
  • About: National Conference on Data Science and Intelligence Information Technology (NCDSIIT) aimed to be a flagship gathering for Data-driven intelligent analysis, sensor networking, telecommunications, health-care and cloud research analysts. 
  • Event Date: 6-7th April 2018
  • Venue: Rajalakshmi Institute of Technology, Kuthambakkam, Chennai
  • Days of Program: Two
  • Timings: 10:00 AM onwards 
  • Purpose: The conference brought together industry practitioners, researchers and potential users of data science to promote the exchange of ideas, collaborations, and practices, investigate actionable analytics and frameworks for a wide number of applications.
  • Registration cost: INR 1250

2. Artificial Intelligence Summit, Chennai, India

Data Scientist Jobs in Chennai, India

To get a job in the field of data science, you need to follow the below-mentioned learning path:

  1. Getting started: Select the programming language that you are comfortable working in. R and Python are most common programming languages. Also, you need to understand what is data science and what your roles and responsibilities will be as a data scientist.
  2. Mathematics: You need to have an understanding of basic algebra and statistics for deciphering patterns and relationships in the data and making sense out of it. You need to be familiar with topics like probability, linear algebra, and descriptive and inferential statistics.
  3. Libraries: Libraries are used for data preprocessing, plotting of structured data and applying algorithms to it. Some of the famous libraries include:
    1. Scikit-learn
    2. ggplot2
    3. NumPy
    4. Pandas
    5. Matplotlib
    6. SciPy
  4. Data visualization: After the analysis is done, data visualization is performed to make the data understandable. To accomplish this, graphs and charts are used. Here are some libraries that are used for data visualization:
    1. Ggplot2 - R
    2. Matplotlib - Python
  5. Data preprocessing: Before any analysis can be done on the data, it goes through preprocessing. This step helps convert unstructured data to structured data. Feature engineering and variable selections are used for this.
  6. ML and Deep learning: Machine learning and deep learning skills are a must to get a job as a data scientist. Make sure that you have a thorough understanding of topics like RNN, CNN, and neural networks.
  7. Natural Language processing: To analyze the textual data, accomplish the task, natural language processing is used.
  8. Polishing skills: To practice and polish your data science skills, you can either try participating in online competitions or take on new projects.

If you are preparing for a data scientist job, you need to follow the below-mentioned steps:

  • Study: You need to brush up on data science topics. Apart from that, you need to focus on other subjects like probability, machine learning, neural networks, statistics, and statistical models.
  • Meetups and conferences: Start visiting data science meetups, conferences, and tech talks. This will help you build your network and expand your professional connections. You will require these connections for referrals.
  • Competitions: Participate in online competitions like kaggle that will help you practice your data science skills.
  • Referral: Referrals have become the main source of the interview in the IT sector. Make sure that your LinkedIn profile is well maintained and updated.
  • Interview: Once you feel you are ready, go for the interview. Learn from the mistakes you made in the interview and study better for the next one.

Here are the major roles and responsibilities of a Data Scientist:

  • Gathering the data that is required for analysis to meet the business’ needs.
  • Next step is the extraction of the relevant data from the gathered data. This also involves organizing the data.
  • After this, you need to create the tools, programs, and techniques required for performing the data analysis.
  • The last step is to perform statistical analysis on the data to get insights and predict future outcomes.

Cognizant - CTS, Tata Consultancy Service - TCS, HCL Technologies, Accenture, Hexaware Technologies, Aspire Systems, Nokia, and Computer Science Corporation - CSC, etc. are some of the leading companies in Chennai looking for skilled data scientists. The average salary for a Data Scientist is ₹11,14,947 in Chennai, India.

The Data Science career path is as follows:

Business Intelligence Analyst: It is the responsibility of a business intelligence analyst to figure out how the business works and its standing in the current market. He also needs to perform data analysis on how market trends affect the business.

Data Mining Engineer: A job of data mining engineer includes examining the collected data and creating sophisticated algorithms required for data analysis.

Data Architect: A data architect creates blueprints with the help of developers and system designers that are used for integrating, centralizing, maintaining, and protecting the data sources.

Data Scientist: A Data Scientist is responsible for analyzing the data, creating a hypothesis, and exploring patterns and relationships present in the data. They also provide insights from data by creating systems and algorithms.

Senior Data Scientist: A senior data scientist has to determine the future needs of the business and make sure that all the projects are shaped in such a way as to reach the goal of the organization.

Given below are the top professional organizations for data scientists in Chennai – 

  • Chennai Big Data Meetup
  • Big Data Developers in Chennai
  • Chennai Big Data Training Meetup
  • Analytics.Club Chennai
  • Know your Future - Machine Learning, AI & Predictive Analytics

The most effective ways to get hired as a data scientist are referrals. Some of the other ways to network with data scientists are:

  • Data Science Conferences
  • Online platforms like LinkedIn
  • Social Gatherings like Meetups

There are several career options for a data scientist –

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

There are some tools and software that you must master to get preferred over other candidates:

  • Education: A degree in Data Science from a prestigious institution will set your CV apart from others. You can even add some certifications to that list.
  • Programming: Programming skills are a must to get a job as a data scientist. Begin with the basics of the language and then slowly move towards more complicated topics. You will also have to learn data science libraries.
  • Machine Learning: Needless to say, Machine learning and deep learning skills are an important requirement for getting hired as a data scientist. This will help you perform data analysis by helping in creating the required tools and frameworks.
  • Projects: The more projects you take on, the better will be your implementation skills. This will also give you an understanding of how real-world projects work.

Data Science with Python Chennai, India

When it comes to data science, python is the most popular and preferred programming language. Here is why:

Python has multiple facets that can be used in the field of data science. It is a structured and object-oriented programming language. Several libraries and packages come in handy while working in the field of data science. The syntax of Python is easy to read, understand, and write. The reason why so many data scientists are attracted towards the programming language is a large number of analytical libraries and packages that comes with it. There are several resources available on the language like documentation, tutorials, videos that can be used by data scientists whenever they are stuck.

The 5 most popular programming languages used for Data Science include:

  • R: R has several advantages that make it one of the most popular programming languages used in data science. It facilitates the smooth processing of complex matrix operations using its multiple statistical functions. With ggplot2, R offers data visualization as well. The R global community provides several open-source, high-quality packages. However, it has a steep learning curve.
  • Python: Python is the most sought-after programming language used in Data Science. It has an easy to read and write syntax that resembles the English language. Python libraries like Pandas, scikit-learn, and tensorflow are very helpful while working in Data Science projects. It is also supported by its big, global, and open-source community.
  • SQL: Knowledge of Structured Query Language or SQL is required to work with relational databases. It has an easy syntax that allows easy querying, updating, and manipulating of data.
  • Java: Java is a compiled, high-performance and general-purpose language that is very compatible. There are several systems in working that is coded in Java so integrating data science projects to it is easier. However, it has limited verbosity and libraries that offer some disadvantages.
  • Scala: This language is used in several data science projects. It has a difficult syntax. But since it runs on JVM, it makes it compatible with Java. When used with the Apache Spark framework, it offers high-performance cluster computing.

Here is what you need to do to download and install Python 3 on Windows:

  • Download and setup: Visit the download page and using the GU installer, install python on Windows. Select the box that asks for adding Python 3.x to PATH. This will allow you to use python’s functionalities from the terminal itself.

  • For checking which version of python is installed on your windows, type in the following command:

python --version

  • Update and install setuptools and pip: For updating and installing important libraries, use the following command:

python -m pip install -U pip

To download and install Python 3 on Mac OS X, all you need to do is:

  1. Install Xcode: The first step is installing the Xcode package of Apple. Type in the following command: 
    $ Xcode-select --install
  2. Install brew: Install the package manager of Apple, Homebrew using the following command: 
    /usr/bin/ruby -e "$(curl -fsSL" Confirm the installation by using the command: brew doctor
  3. Install Python 3: To install python, type in the following: brew install python

To confirm if python was installed, use the command: python --version

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The content was sufficient and the trainer was well-versed in the subject. Not only did he ensure that we understood the logic behind every step, he always used real-life examples to make it easier for us to understand. Moreover, he spent additional time to let us consult him on Data Science-related matters outside the curriculum. He gave us advice and extra study materials to enhance our understanding. Thanks, KnowledgeHut!

Ong Chu Feng

Data Analyst
Attended Data Science with Python Certification workshop in January 2020
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I had enrolled for the course last week at KnowledgeHut. The course was very well structured. The trainer was really helpful and completed the syllabus on time and also provided real world examples which helped me to remember the concepts.

York Bollani

Computer Systems Analyst.
Attended Agile and Scrum workshop in May 2018
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The instructor was very knowledgeable, the course was structured very well. I would like to sincerely thank the customer support team for extending their support at every step. They were always ready to help and smoothed out the whole process.

Astrid Corduas

Telecommunications Specialist
Attended Agile and Scrum 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 was very patient and well informed.

Anabel Bavaro

Senior Engineer
Attended Certified ScrumMaster (CSM)® workshop in May 2018
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The Trainer at KnowledgeHut made sure to address all my doubts clearly. I was really impressed with the training and I was able to learn a lot of new things. I would certainly recommend it to my team.

Meg Gomes casseres

Database Administrator.
Attended PMP® Certification workshop in May 2018
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The teaching methods followed by Knowledgehut is really unique. The best thing is that I missed a few of the topics, and even then the trainer took the pain of taking me through those topics in the next session. I really look forward to joining KnowledgeHut soon for another training session.

Archibold Corduas

Senior Web Administrator
Attended Certified ScrumMaster (CSM)® workshop in May 2018
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It is always great to talk about Knowledgehut. I liked the way they supported me until I got certified. 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. My special thanks to the trainer for his dedication and patience.

Ellsworth Bock

Senior System Architect
Attended Certified ScrumMaster (CSM)® workshop in May 2018
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The workshop was practical with lots of hands on examples which has given me the confidence to do better in my job. I learned many things in that session with live examples. The study materials are relevant and easy to understand and have been a really good support. I also liked the way the customer support team addressed every issue.

Marta Fitts

Network Engineer
Attended PMP® Certification workshop in May 2018


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. 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

Have More Questions?

Data Science with Python Certification Course in Chennai

Chennai is the biggest industrial and commercial centre in South India, and a major cultural, economic and educational zone. It is the capital city of the Indian state of Tamil Nadu with a thriving business environment. Given its strong auto manufacturing industry, it is known as the ?Detroit of India?. The city is host to the third-largest expatriate population in India after Mumbai and Delhi; it has played a very crucial role in the traditional, historical and academic growth of the country, representing the different aspects of the highest variety of the Dravidian culture. The praise of the booming economy of the city goes to the leading industries including software services, petrochemicals, financial services, textiles and hardware manufacturing. The city offers great prospects for those seeking a career in project management, Big Data and Hadoop and many others. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.