Data Science with Python Training in Chennai, India

Get hands-on Python skills and accelerate your data science career

  • Learn Python, analyze and visualize data with Pandas, Matplotlib and Scikit.
  • Create robust predictive models with advanced statistics.
  • Leverage hypothesis testing and inferential statistics for sound decision-making.
  • 250,000 + Professionals Trained
  • 55,000 + Programmers upskilled
  • 70 + Countries and counting

Grow your Data Science skills

This four-week course takes you from the fundamentals of Data Science to an advanced level. Get hands-on programming experience in Python that you'll be able to immediately apply in the real world. Equip yourself with the skills you need to work with large data sets, build predictive models and tell a compelling story to stakeholders.

..... Read more
Read less

Highlights

  • 42 Hours of Live Instructor-Led Sessions

  • 60 Hours of Assignments and MCQs

  • 36 Hours of Hands-On Practice

  • 6 Real-World Live Projects

  • Fundamentals to Advanced Learning

  • Code Reviews by Professionals

Why Become a Data Scientist?

Data Science has bagged the top spot in LinkedIn’s Emerging Jobs Report for the last three years. Thousands of companies need team members who can transform data sets into strategic forecasts. Acquire in-demand data science and Python skills and meet that need.

..... Read more
Read less

Not sure how to get started? Let our Learning Advisor help you.

Contact Learning Advisor

The KnowledgeHut Edge

Learn by Doing

Our immersive learning approach lets you learn by doing and acquire immediately applicable skills hands-on.

Real-World Focus

Learn theory backed by real-world practical case studies and exercises. Skill up and get productive from the get-go.

Industry Experts

Get trained by leading practitioners who share best practices from their experience across industries.

Curriculum Designed by the Best

Our Data Science advisory board regularly curates best practices to emphasize real-world relevance.

Exclusive Post-Training Sessions

Practical one-to-one guidance from mentors: project review and evaluation, guidance on work challenges.

Continual Learning Support

Webinars, e-books, tutorials, articles, and interview questions - we're right by you in your learning journey!

Prerequisites

Prerequisites for the Data Science with Python training program

  • There are no prerequisites to attend this course.
  • Elementary programming knowledge will be useful.

Who should attend this course?

Anyone interested in the field of data science

Anyone looking for a more robust, structured Python learning program

Anyone looking to use Python for effective analysis of large datasets

Software or data engineers interested in quantitative analysis with Python

Data analysts, economists or researchers

Data Science with Python Course Schedules

100% Money Back Guarantee

Can't find the batch you're looking for?

Request a Batch

What you will learn in the Data Science with Python course

1

Python Distribution

Anaconda, basic data types, strings, regular expressions, data structures, loops, and control statements.

2

User-defined functions in Python

Lambda function and the object-oriented way of writing classes and objects.

3

Datasets and manipulation

Importing datasets into Python, writing outputs and data analysis using Pandas library.

4

Probability and Statistics

Data values, data distribution, conditional probability, and hypothesis testing.

5

Advanced Statistics

Analysis of variance, linear regression, model building, dimensionality reduction techniques.

6

Predictive Modelling

Evaluation of model parameters, model performance, and classification problems.

7

Time Series Forecasting

Time Series data, its components and tools.

Skill you will gain with the Data Science with Python course

Python programming skills

Manipulating and analysing data using Pandas library

Data visualization with Matplotlib, Seaborn, ggplot

Data distribution: variance, standard deviation, more

Calculating conditional probability via hypothesis testing

Analysis of Variance (ANOVA)

Building linear regression models

Using Dimensionality Reduction Technique

Building Binomial Logistic Regression models

Building KNN algorithm models to find the optimum value of K

Building Decision Tree models for regression and classification

Visualizing Time Series data and components

Exponential smoothing

Evaluating model parameters

Measuring performance metrics

Transform Your Workforce

Harness the power of data to unlock business value

Invest in forward-thinking data talent to leverage data’s predictive power, craft smart business strategies, and drive informed decision-making.

  • Immersive Learning with a Learn-by-Doing approach
  • Applied Learning to get your teams project-ready
  • Align skill development to your most important objectives
  • Upskill your teams into modern roles with Customized Training Solutions
Skill Up Your Teams
500+ Clients

Learning objectives

Understand the basics of Data Science and gauge the current landscape and opportunities. Get acquainted with various analysis and visualization tools used in data science.


Topics

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

Learning objectives

The Python module will equip you with a wide range of Python skills. You will learn to:

  • To Install Python Distribution - Anaconda, basic data types, strings, and regular expressions, data structures and loops, and control statements that are used in Python
  • To write user-defined functions in Python
  • About Lambda function and the object-oriented way of writing classes and objects 
  • How to import datasets into Python
  • How to write output into files from Python, manipulate and analyse data using Pandas library
  • Use Python libraries like Matplotlib, Seaborn, and ggplot for data visualization

Topics

  • Python Basics
  • Data Structures in Python 
  • Control and Loop Statements in Python
  • Functions and Classes in Python
  • Working with Data
  • Data Analysis using Pandas
  • Data Visualisation
  • Case Study

Hands-on

  • How to install Python distribution such as Anaconda and other libraries
  • To write python code for defining as well as executing your own functions
  • The object-oriented way of writing classes and objects
  • How to write python code to import dataset into python notebook
  • How to write Python code to implement Data Manipulation, Preparation, and Exploratory Data Analysis in a dataset

Learning objectives

In the Probability and Statistics module you will learn:

  • Basics of data-driven values - mean, median, and mode
  • Distribution of data in terms of variance, standard deviation, interquartile range
  • Basic summaries of data and measures and simple graphical analysis
  • Basics of probability with real-time examples
  • Marginal probability, and its crucial role in data science
  • Bayes’ theorem and how to use it to calculate conditional probability via Hypothesis Testing
  • Alternate and Null hypothesis - Type1 error, Type2 error, Statistical Power, and p-value

Topics

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

Hands-on

  • How to write Python code to formulate Hypothesis
  • How to perform Hypothesis Testing on an existent production plant scenario

Learning objectives

Explore the various approaches to predictive modelling and dive deep into advanced statistics:

  • Analysis of Variance (ANOVA) and its practicality
  • Linear Regression with Ordinary Least Square Estimate to predict a continuous variable
  • Model building, evaluating model parameters, and measuring performance metrics on Test and Validation set
  • How to enhance model performance by means of various steps via processes such as feature engineering, and regularisation
  • Linear Regression through a real-life case study
  • Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis
  • Various techniques to find the optimum number of components or factors using screen plot and one-eigenvalue criterion, in addition to a real-Life case study with PCA and FA.

Topics

  • Analysis of Variance (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 for which you are required to build a regression model to predict the property prices
  • Reducing Dimensionality of a House Attribute Dataset to achieve more insights and better modelling

Learning objectives

Take your advanced statistics and predictive modelling skills to the next level in this advanced module covering:

  • Binomial Logistic Regression for Binomial Classification Problems
  • Evaluation of model parameters
  • Model performance using various metrics like sensitivity, specificity, precision, recall, ROC Curve, AUC, KS-Statistics, and Kappa Value
  • Binomial Logistic Regression with a real-life case Study
  • KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K
  • KNN through a real-life case study
  • Decision Trees - for both regression & classification problem
  • Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID
  • Using Decision Tree with real-life Case Study

Topics

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

Hands-on

  • Building a classification model to predict which customer is likely to default a credit card payment next month, based on various customer attributes describing customer characteristics
  • Predicting if a patient is likely to get any chronic kidney disease depending on the health metrics
  • Building a model to predict the Wine Quality using Decision Tree based on the ingredients’ composition

Learning objectives

All you need to know to work with time series data with practical case studies and hands-on exercises. You will:

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

Topics

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

Hands-on

  • Writing python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
  • Writing 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.
  • Writing Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Use ARIMA to predict the stock prices based on the dataset including features such as symbol, date, close, adjusted closing, and volume of a stock.

Learning objectives

This industry-relevant capstone project under the experienced guidance of an industry expert is the cornerstone of this Data Science with Python course. In this immersive learning mentor-guided live group project, you will go about executing the data science project as you would any business problem in the real-world.


Hands-on

  • Project to be selected by candidates.

Frequently Asked Questions

Data Science with Python Training

The Data Science with Python course has been thoughtfully designed to make you a dependable Data Scientist ready to take on significant roles in top tech companies. At the end of the course, you will be able to:

  • Build Python programs: distribution, user-defined functions, importing datasets and more
  • Manipulate and analyse data using Pandas library
  • Data visualization with Python libraries: Matplotlib, Seaborn, and ggplot
  • Distribution of data: variance, standard deviation, interquartile range
  • Calculating conditional probability via Hypothesis Testing
  • Analysis of Variance (ANOVA)
  • Building linear regression models, evaluating model parameters, and measuring performance metrics
  • Using Dimensionality Reduction Technique
  • Building Binomial Logistic Regression models, evaluating model parameters, and measuring performance metrics
  • Building KNN algorithm models to find the optimum value of K
  • Building Decision Tree models for both regression and classification problems
  • Build Python programs: distribution, user-defined functions, importing datasets and more
  • Manipulate and analyse data using Pandas library
  • Visualize data with Python libraries: Matplotlib, Seaborn, and ggplot
  • Build data distribution models: variance, standard deviation, interquartile range
  • Calculate conditional probability via Hypothesis Testing
  • Perform analysis of variance (ANOVA)
  • Build linear regression models, evaluate model parameters, and measure performance metrics
  • Use Dimensionality Reduction
  • Build Logistic Regression models, evaluate model parameters, and measure performance metrics
  • Perform K-means Clustering and Hierarchical Clustering
  • Build KNN algorithm models to find the optimum value of K
  • Build Decision Tree models for both regression and classification problems
  • Build data visualization models for Time Series data and components
  • Perform exponential smoothing

The program is designed to suit all levels of Data Science expertise. From the fundamentals to the advanced concepts in Data Science, the course covers everything you need to know, whether you’re a novice or an expert. To facilitate development of immediately applicable skills, the training adopts an applied learning approach with instructor-led training, hands-on exercises, projects, and activities.

Yes, our Data Science with Python course is designed to offer flexibility for you to upskill as per your convenience. We have both weekday and weekend batches to accommodate your current job.

In addition to the training hours, we recommend spending about 2 hours every day, for the duration of course.

The Data Science with Python course is ideal for:

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

There are no prerequisites for attending this course, however prior knowledge of elementary programming, preferably using Python, would prove to be handy.

To attend the Data Science with Python training program, the basic hardware and software requirements are as mentioned below -

Hardware requirements

  • Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or latest version of other popular Linux flavors
  • 4 GB RAM
  • 10 GB of free space

Software Requirements

  • Web browser such as Google Chrome, Microsoft Edge, or Firefox

System Requirements

  • 32 or 64-bit Operating System
  • 8 GB of RAM

On adequately completing all aspects of the Data Science with Python course, you will be offered a course completion certificate from KnowledgeHut.

In addition, you will get to showcase your newly acquired data-handling and programming skills by working on live projects, thus, adding value to your portfolio. The assignments and module-level projects further enrich your learning experience. You also get the opportunity to practice your new knowledge and skillset on independent capstone projects.

By the end of the course, you will have the opportunity to work on a capstone project. The project is based on real-life scenarios and carried-out under the guidance of industry experts. You will go about it the same way you would execute a data science project in the real business world.

Data Science with Python Workshop

The Data Science with Python workshop at KnowledgeHut is delivered through PRISM, our immersive learning experience platform, via live and interactive instructor-led training sessions.

Listen, learn, ask questions, and get all your doubts clarified from your instructor, who is an experienced Data Science and Machine Learning industry expert.

The Data Science with Python course is delivered by leading practitioners who bring trending, best practices, and case studies from their experience to the live, interactive training sessions. The instructors are industry-recognized experts with over 10 years of experience in Data Science. 

The instructors will not only impart conceptual knowledge but end-to-end mentorship too, with hands-on guidance on the real-world projects.

Our Date Science course focuses on engaging interaction. Most class time is dedicated to fun hands-on exercises, lively discussions, case studies and team collaboration, all facilitated by an instructor who is an industry expert. The focus is on developing immediately applicable skills to real-world problems.

Such a workshop structure enables us to deliver an applied learning experience. This reputable workshop structure has worked well with thousands of engineers, whom we have helped upskill, over the years. 

Our Data Science with Python workshops are currently held online. So, anyone with a stable internet, from anywhere across the world, can access the course and benefit from it.

Schedules for our upcoming workshops in Data Science with Python can be found here.

We currently use the Zoom platform for video conferencing. We will also be adding more integrations with Webex and Microsoft Teams. However, all the sessions and recordings will be available right from within our learning platform. Learners will not have to wait for any notifications or links or install any additional software.

You will receive a registration link from PRISM to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our Immersive Learning Experience platform and start your educational journey.

Yes, there are other participants who actively participate in the class. They remotely attend online training from office, home, or any place of their choosing.

In case of any queries, our support team is available to you 24/7 via the Help and Support section on PRISM. You can also reach out to your workshop manager via group messenger.

If you miss a class, you can access the class recordings from PRISM at any time. At the beginning of every session, there will be a 10-12-minute recapitulation of the previous class.

Should you have any more questions, please raise a ticket or email us at support@knowledgehut.com and we will be happy to get back to you.

What Learners Are Saying

Ong Chu Feng

Ong Chu Feng

Data Analyst

4/5

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 un View More

Attended Data Science with Python Certification workshop in January 2020

Issy Basseri

Issy Basseri

Database Administrator

5/5

Knowledgehut is the best training institution. The advanced concepts and tasks during the course given by the trainer helped me to step up in my career. He used to ask for feedback every time and clear all the View More

Attended PMP® Certification workshop in January 2020

Merralee Heiland

Merralee Heiland

Software Developer.

5/5

KnowledgeHut is a great platform for beginners as well as experienced professionals who want to get into the data science field. Trainers are well experienced and participants are given detailed ideas and conce View More

Attended PMP® Certification workshop in April 2020

Steffen Grigoletto

Steffen Grigoletto

Senior Database Administrator

5/5

Everything was well organized. I would definitely refer their courses to my peers as well. The customer support was very interactive. As a small suggestion to the trainer, it will be better if we have discussio View More

Attended PMP® Certification workshop in April 2020

Rafaello Heiland

Rafaello Heiland

Prinicipal Consultant

5/5

I am really happy with the trainer because the training session went beyond my expectations. Trainer has got in-depth knowledge and excellent communication skills. This training has actually prepared me for my View More

Attended Agile and Scrum workshop in April 2020

Tilly Grigoletto

Tilly Grigoletto

Solutions Architect.

5/5

I really enjoyed the training session and am extremely satisfied. All my doubts on the topics were cleared with live examples. KnowledgeHut has got the best trainers in the education industry. Overall the sessi View More

Attended Agile and Scrum workshop in February 2020

Godart Gomes casseres

Godart Gomes casseres

Junior Software Engineer

5/5

Knowledgehut is known for the best training. I came to know about Knowledgehut through one of my friends. I liked the way they have framed the entire course. During the course, I worked on many projects and lea View More

Attended Agile and Scrum workshop in January 2020

Matteo Vanderlaan

Matteo Vanderlaan

System Architect

5/5

I was totally impressed by the teaching methods followed by Knowledgehut. The trainer gave us tips and tricks throughout the training session. The training session gave me the confidence to do better in my job. View More

Attended Certified ScrumMaster (CSM)® workshop in January 2020

Career Accelerator Bootcamps

Trending
Full Stack Developer Career Track Bootcamp
  • 132+ hours of live and interactive sessions by industry experts
  • Immersive Learning with Guided Hands-on Exercises (Cloud Labs)
  • 132 Hrs
  • 4.5
BECOME A SKILLED DEVELOPER SKILL UP NOW
Front-end Development Bootcamp
  • 80 hours of comprehensive hands-on Front End Development training
  • Work on 5 real-time projects & multiple assignments from experts
  • 4.5
BECOME A SKILLED DEVELOPER SKILL UP NOW

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 https://raw.githubusercontent.com/Homebrew/install/master/install)" 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

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.

Other Training

100% MONEY-BACK GUARANTEE!

Want to cancel?

Withdrawal

Transfer