Data Science with Python Training in Mumbai, 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.
  • 220,000 + Professionals Trained
  • 250 + Workshops every month
  • 70 + Countries and counting

Grow your Data Science skills

This comprehensive hands-on course takes you from the fundamentals of Data Science to an advanced level in weeks. 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.

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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 an Advanced Level

  • Code Reviews by Professionals

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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.

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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.

Continual Learning Support

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

Exclusive Post-Training Sessions

Six months of post-training mentor guidance to overcome challenges in your Data Science career.

Prerequisites

Prerequisites for the Data Science with Python training program

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

Who should attend this course?

Professionals in the field of data science

Professionals looking for a robust, structured Python learning program

Professionals working with large datasets

Software or data engineers interested in quantitative analysis

Data analysts, economists, researchers

Data Science with Python Course Schedules

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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

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Data Science with Python Course Curriculum

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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.

FAQs on the Data Science with Python Course

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

O

Ong Chu Feng

Data Analyst

4

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!

Attended Data Science with Python Certification workshop in January 2020

I

Issy Basseri

Database Administrator

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 doubts.

Attended PMP® Certification workshop in January 2020

S

Steffen Grigoletto

Senior Database Administrator

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 discussions in the end like Q&A sessions.

Attended PMP® Certification workshop in April 2020

Y

Yancey Rosenkrantz

Senior Network System Administrator

5

The customer support was very interactive. The trainer took a very practical oriented session which is supporting me in my daily work. I learned many things in that session. Because of these training sessions, I would be able to sit for the exam with confidence.

Attended Agile and Scrum workshop in April 2020

G

Godart Gomes casseres

Junior Software Engineer

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 learned many things which will help me to enhance my career. The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut.

Attended Agile and Scrum workshop in January 2020

A

Anabel Bavaro

Senior Engineer

5

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.

Attended Certified ScrumMaster (CSM)® workshop in August 2020

G

Goldina Wei

Java Developer

5

Knowledgehut is the best platform to gather new skills. Customer support here is very responsive. The trainer was very well experienced and helped me in clearing the doubts clearly with examples.

Attended Agile and Scrum workshop in June 2020

T

Tilly Grigoletto

Solutions Architect.

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 session was a great experience.

Attended Agile and Scrum workshop in February 2020

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Data Science with Python

What is Data Science

Data science job is considered as one of the hottest jobs of the 21st century because of the various benefits it offers. A skilled data scientist is the need of today's competitive market. The job involves developing various methods by which data can be collected so that useful and relevant information can be sorted out. The main motive of data science is to solve analytically complex problems and to obtain insights from any type of data, whether specific or non-specific. Data Science is also often used interchangeably with earlier concepts like business analytics, business intelligence, predictive modeling, and statistics. Mumbai is a great place to be for an aspiring data scientist as it has some of the most recognized companies in the field such as Google, S2 Infotech, BookMyShow, Tata CLiQ, CreditMate, JP Morgan Chase & Co, Truebil etc. 

Mumbai is known for being home to some of India’s most prestigious universities like IIT, Aegis School of Data Science and Cyber Security, Mumbai University etc., that offer data science courses. 

These are the top 8 technical skills needed to become a Data Scientist. These are mandatory for any data scientist to have no matter which city he/she is based in -

  • R Programming
  • Python coding
  • Big Data Hadoop
  • Data Visualization
  • SQL database and coding
  • Machine Learning and Artificial Intelligence
  • Apache Spark
  • Unstructured data

  1. R Programming- Programming based on R language is generally preferred to solve statistical problems. Language R is specifically designed for data science needs. It is extensively used for developing statistical software and data analysis. As it is more user-friendly and performs well in developing graphics modules and statistics, it became a highly-preferred skill for data scientists.
  2. Python coding- After JAVA and C/C++, Python is the most common coding language required in data science. Today it is very actively practiced among the active developers and, it has even become essential for some entry-level jobs. Python coding is so versatile that it can be used in almost all the steps of data science.
  3. Big Data Hadoop- Hadoop is not a compulsory requirement for Data science, but it is preferred in many projects. Hadoop is used to convey data to various sections of a system or servers. It is required when the volume of the data exceeds the memory of the system, and the rest of the data has to be located by sharing on different servers. 
  4. Data Visualization- There are a huge number of data available in raw form in the market. It is the work of a Data Scientist to transcribe these data in the form that others may be able to visualize and understand it quickly. Tools like- ggplot, d3.js and Matplottlib, and Tableau may be helpful in data visualization.
  5. SQL Database and coding- SQL is a programming language that assists in carrying out processes like add, delete and extract data from a database. It helps data scientists to access, communicate as well as work on data. MySQL also supports short commands that can save time and decrease the level of technical skills required to perform operations on a database.
  6. Machine Learning and Artificial Intelligence- To become a skilled data scientist, one should have thorough knowledge about machine learning and AI. With the growing demand and competition in the market, enterprises are finding the newest and user-friendly ideas to include in their products, and this is giving rise to the application of ML and AI in achieving new heights. Thus, a data scientist should be competent in ML & AI skills.
  7. Apache Spark- In recent times, Apache Spark is one of the most popular big data technologies worldwide. Its working is like Hadoop but it works faster than Hadoop. The data science algorithm works faster with Apache Spark. It also makes it possible for data scientists to prevent loss of data in data science. Unstructured data sets can be handled easily with the help of this big data technology.
  8. Unstructured Data- Internet is full of unstructured data, and the main job of a data scientist is to proof, sort, analyze and visualize such data in a structured form and present in front of others. Working with unstructured data is the core of a data science job.

The top 5 behavioral traits of a successful Data Scientist are -

  • Curiosity – A curious person will always find a way to learn and explore. This attitude helps a data scientist in moving forward without getting bored with their job.
  • Clarity – Data Scientists need a clear picture of data. And so professionals should also be clear about their paths and methods of curating and using such data.
  • Creativity - A data scientist should be creative enough in finding new ways to accumulate data which is required. Obtaining data is not as easy a job as it looks. It needs several uncalled methods, and a professional should be able to create these methods.
  • Skepticism – Although a data scientist needs to be creative, he also needs to be rational with his thoughts.  Despite advancements in technology every data scientist should know the limits of any resources/methods and should not get carried away with his/her own views alone.

There are many benefits to being in the job declared as the ‘Sexiest job of the 21st century’ by Harvard Business review in 2012:

  1. High Pay: There is a high demand for a skilled data scientist in the IT and business industry. Compared to the demand, the supply of such skilled employees is much less and that makes it a high paying job. Not only the demand-supply chain but the qualification they possess, also matters and they get paid for it. The average pay in Mumbai is Rs 575,295 per year.
  2. Good bonuses: One's loss is another's gain and no company wants to be on the losing side. To keep their highly qualified employees, the company will surely give them many perks from time to time. Data scientists can also expect impressive bonuses.
  3. Education: Being a data scientist means, you would be well-educated and might need to have either a Masters or a Ph.D. due to the demand for knowledge in this field. Thus, this knowledge will open various paths for you. Not only can you become an employee in a big company, but you could receive offers to work as a lecturer or as a researcher for governmental as well as private institutions.
  4. Mobility: Having in-depth knowledge and experience will give you an upper hand in choosing whether to work as a freelancer or as a full-time employee. Most of the businesses that collect data are located in developed countries and by getting a job there, you will get a high salary that will raise your standard of living.
  5. Network: Publishing research papers in international journals, attending tech talks at conferences, and many more such opportunities will help to expand your network of data scientists.

Data Scientist Skills and Qualifications

Below are the must-have business skills you need to become a data scientist-

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

  • Analytic Problem-Solving – Business needs proper analysis to solve their problems. A data scientist should have such skills to analyze the data that is generated by the business.
  • Communication Skills – One of the key responsibilities of a data scientist is to communicate customer analytics and business trends to companies. A data scientist should have good communication skills so that he can properly share his results with the company.
  • Intellectual Curiosity- Having intellectual curiosity as a trait is helpful for both the employee and for the company. A person needs curiosity in finding the data, only then, the work will be done efficiently.
  • Industrial Knowledge – Last, but not least, this is perhaps one of the most important skills. Having a solid industrial knowledge clears out the idea of what is required and what should be ignored.

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

  • Practicing short coding challenges: Regular sessions of practice helps in maintaining focus and finding the solution.
  • Boot camps: Boot camps about Python and other languages are the perfect ways to brush up the programming basics. These camps last not more than a week. It gives the chance of getting theoretical and practical knowledge.
  • Online courses: These are online courses and introduce some of the latest trends in the industry. These are generally taught by data science experts and help to get in touch with the latest technological trends.
  • Certificate courses: Learning new data science courses will provide you with an additional skill set and helps improve your CV significantly.
  • Competitions: Participating in competitions like Kaggle etc. help in improving your problem-solving skills.  Additionally, participating in a competition will also increase the value of your CV.

Mumbai is one of the most advanced cities in India. It is home to some of the most prominent universities and leading companies such as Google, S2 Infotech, BookMyShow, Tata CLiQ, CreditMate, JP Morgan Chase & Co, Truebil, etc. which offers data science. As of now, every foresighted company needs data. 

There are several datasets available that you can use for practicing your data science skills. Here we have compiled a list of datasets categorized according to their difficulty level and your expertise level:

  • Beginner Level
    • Iris Data Set: Consisting of 4 columns and 50 rows, this dataset is the easiest, most versatile and resourceful dataset. It is a classification problem that is used for pattern recognition. Practice Problem: Using the parameters for predicting the flower’s class.
    • Bigmart Sales Data Set: This dataset from the retail industry contains 12 variables and 85,223 rows. Operations like product bundling, customizations, inventory management, etc. are carried out using business analytics and data science. It is a regression problem. Practice Problem: Predicting the total sales of the store.
  • Intermediate Level:
    • Black Friday Data Set: It is a regression problem with 12 columns and 550,069 rows. This will help you understand the shopping experience of millions of customers.Practice Problem: Predicting the total amount of purchase.
    • Human Activity Recognition Data Set: This dataset consists of 561 columns and 10,299 rows which was collected using smartphone recording of 30 people. Inertial sensors were embedded in each smartphone.Practice Problem: Predicting the category of human activity. 
  • Advanced Level:
    • Identify the digits data set: This dataset contains 7000 images of 82X28 dimensions each. It includes studying, analyzing, and recognizing each element present in the image.Practice Problem: Identifying the different elements present in the image.
    • Vox Celebrity Data Set: This dataset is for practicing isolating and isolating speech. You will be using audio processing in deep learning. With 100,000 words from 1,251 celebrities, this large scale speaker identification problem was extracted from YouTube videos.Practice Problem: Identifying the voice of the celebrity.

How to Become a Data Scientist in Mumbai, India

Here are the right steps that you need to follow to become a top-notch data scientist:

  1. Getting started: The first step is to choose a programming language. R and Python are the most preferred programming languages used in the field of Data Science.
  2. Mathematics and statistics: One must have the skills of mathematics and statistics to analyze the data, decipher patterns in it and figure out the relationship among them.
  3. Data visualization: Data visualization is required to make the data understandable for the non-technical members. It is also a great way to communicate with the end users.
  4. ML and Deep learning: Machine learning and deep learning skills are needed for creating the tools to perform the analysis of the data.

Below are the right steps to becoming a successful data scientist:

  1. Degree/certificate: Mumbai is known for being home to some of India’s most prestigious universities like IIT, Aegis School of Data Science and Cyber Security, Mumbai University etc. You can opt for either online or offline courses. At least, pursue a certification course from a registered training. These classes will be beneficial for your growth and teach you the correct use of methods and tools required in the field of data science. 
  2. Power up your math and statistics skills: Data scientists must be able to understand the world of numbers and stats. Knowing and understanding algorithms and statistical methods should be the priority of a data scientist. 
  3. Learn to code: Coding is the heart of data science and every person who is engaged in sorting out data should have some knowledge about coding. One should start with learning a coding language. Open-source language like Python is recommended here.
  4. Knowing the concept of ML: Machine Learning is linked to the big data and is in high demand in the market. Artificial intelligence algorithms are used by machine learning to turn data into value, without the use of program coding.
  5. Understand data visualization: Data visualization is analyzing, sorting, curating and transcription of raw data into such a form that can be understood by the company. To become a data scientist, one should learn these skills and their applications.
  6. Understand Database: The data is stored in the database and building up of your own data storage is a massive advantage and skill which can be helpful in your career of being a data scientist.

Mumbai is known for being home to some of India’s most prestigious universities like IIT, Aegis School of Data Science and Cyber Security, Mumbai University etc. These global universities offer top courses and degrees in Data Science. Having a degree shows that you have studied and applied most of the concept of data science before applying for a job. This is the reason why almost 88% of data scientists hold a Master’s degree while 46% of all data scientists are PhD degree holders.

A degree is very important because of the following –

  • Networking – When you are a professional, having a wide network becomes an asset for your personal as well as professional growth. While pursuing a degree, you will get the opportunity to make like-minded acquaintances, which will be an aid to your network.
  • Structured learning – Getting a degree and formal education in the field of data science is more effective and beneficial than doing things unplanned.
  • Internships – Enrolling in a course may help you to get a good internship opportunity. An internship helps to get practical knowledge and experience which is the demand of the companies.
  • Required academic qualifications for your résumé – Applying for a job starts from submitting your resume. The most common and prior qualification a company demands on a resume while giving you a job is a formal degree from a recognized institution. A degree from a prestigious institution will not only look good but will also give you a chance to apply for the top jobs.

Knowledge, experience, and capability of a person determines whether they need a master degree or not to become a data scientist. Having a master degree can add value to your resume but it is not always the case. A person can still become a good data scientist without having a master's degree if he excels in other fields, related to data science. Further, having a master degree will add other skills and polish the already existing skills you have. Generally, people go for Master's degree either because they must have come from a different undergraduate program or they want to gain more experience in data sciences.

Knowledge of programming is the most important and basic skill that a data scientist must possess. Other reasons why knowledge in programming is required include:

  • Data sets: Data science involves working with large amounts of data sets. Knowledge of programming aids a data scientist in the analysis of large data sets.
  • Statistics: It needs programming to do the level of statistics that are needed in data sciences. Good knowledge of programming will enhance the ability to solve the statistical problem in a much easier way.
  • Framework: The programming ability of a data scientist also enables him/her to create relevant frameworks that can automatically analyze experiments and visualize data.

Data Scientist Salary in Mumbai, India

A Data Scientist earns an average annual salary of Rs. 6,72,492 in Mumbai.

As opposed to the Data scientist’s average annual salary of Rs. 6,72,492 in Mumbai, Data Scientists in Delhi earn about Rs. 9,92,129 annually.

The average annual earnings of a Data Scientist in Mumbai is Rs. 6,72,492  as compared to Rs. 6,15,496 earned by a Data Scientist in Bangalore.

A Data Scientist in Mumbai earns about Rs. 6,72,492 every year as compared to Rs. 8,19,815 earned by a Data Scientist in Chennai.

The average annual salary of a Data Scientist in Mumbai is Rs. 6,72,492. While the same in Pune, a major city in Maharashtra, is Rs. 5,89,581.

The demand for a Data Scientist in Mumbai is high. Every company produces data on a daily basis and they require trained professionals who can analyze this data for business continuity. The demand for a data scientist is far more than the supply and it’s not going to go down anytime soon.

The primary benefit of working as a Data Scientist in Mumbai is that the city offers so many job opportunities. With plenty of companies embracing big data to help them make important business decisions, the importance of Data Scientists has increased. So higher salaries, better perks and more opportunities can be listed as some of the benefits of being a data scientist in Mumbai.

For a Data Scientist, Mumbai is one of the best cities to work in. There are a number of companies that are looking to invest in Data Science and are looking for Data Scientists to convert their raw numbers into insights. Also, a data scientist doesn’t have to stay bound to a particular field. They can choose a field of their interest because today every company in every field is investing in Data Science. Being one of the major cities in the country, it has a number of data science events organized daily where you can meet fellow data scientists and build your network. 

If you are a data scientist in Mumbai, the companies where you can look for job opportunities include BlackRock, Colgate, Palmolive, Google, Prognoz Technologies Pvt. Ltd., Adoro, BookMyShow, General Mills, Spheno, Cymetrix Software, Accrete.AI, Ketto, Camsdata, Bureau Veritas India, Weatherford and many more. 

Data Science Conference in Mumbai, India

S.NoConference nameDateVenue
1.Gartner Data & Analytics Summit 2019, Mumbai, India10th June - 11th June, 2019Renaissance Mumbai Convention Centre Hotel, 2 & 3B, Near Chinmayanand Ashram, Powai, Mumbai, Maharashtra 400087
2.The MachineCon, 2019, Mumbai, India24th May, 2019Novotal Juhu, Mumbai
3.Data Workshop and Meetup, Pydata initiative, Mumbai, IndiaMay 18, 201991springboard Vikhroli, Opposite Vikhroli Bus Depot, Vikhroli West · Mumbai
4.DataGiri's Code-along Saturdays, Mumbai, India11th May, 2019TBA
5.Deep Learning with Computer Vision in FinTech, Mumbai, India
May 11, 2019
Rise Mumbai 1902, 19th floor Tower B, Peninsula Business Park Lower Parel, Mumbai
6.Machine Learning - A Graphical Intuition, Mumbai, India
12 May, 2019
CETTM - Center for Excellence in Telecom Training and Management, MTNL Technology Street, Hiranandani Gardens, Powai, Mumbai, Maharashtra 400076
7.IDF Mumbai Online Meetup, Mumbai, India
May 17, 2019
Online
8.The Fifth Elephant Winter 2019, Mumbai, India
Friday, 18th Jan, 2019
ISDI ACE, Colab Area, 7th Floor, Tower 2A, One Indiabulls Center, Lower Parel, Mumbai, Maharashtra - 400013
9.Asian Conference on Recent Advances in Science, Engineering and Technology, Mumbai, India
2 May, 2019
Radisson Mumbai Goregaon, Mumbai, India
10.India IOT SUMMIT 2019, Mumbai, India
8th Feb, 2019
Hotel ITC Maratha, Mumbai

1. Gartner Data & Analytics Summit 2019, Mumbai

  • About the conference: The conference will have a discussion on Analytics and how it can bring clarity in a world of ambiguity. 
  • Event Date: 10th - 11th June, 2019
  • Venue: Renaissance Mumbai Convention Centre Hotel, 2 & 3B, Near Chinmayanand Ashram, Powai, Mumbai, Maharashtra 400087
  • Days of Program: Two
  • Timings: 7:15 AM - 8:00 PM
  • Purpose: Develop a foundation of efficient business, running through data quality, security, governance and privacy.
  • Number of speakers: 20+
  • Speaker's profile: 
    • Rita Sallam, Research VP, Gartner 
    • Ted Friedman, Vice President and Distinguished Analyst, Gartner Research 
    • Ehtisham Zaidi Sr Principal Analyst, Gartner 
  • Whom can you Network with in this Conference: Gartner Chief Digital Officer (CDO) Circle, End-user case studies, analyst-user roundtables and Gartner one-on-one meetings.
  • Registration cost: Standard Conference Fee - INR 65,000 + Taxes and Public-Sector Rate - INR 52,500 + Taxes
  • Who are the major sponsors: IBM, Qlik, SAP, CloudEra, Cognizant, NxtGen, etc.

2. The MachineCon, 2019, Mumbai

  • About the conference: The Machine Conference is an exclusive setting for Analytics and Data Science Leaders of Asia. The agenda is to examine the best opportunities in a data powered world.
  • Event Date: 24th May, 2019
  • Venue: Novotal Juhu, Mumbai
  • Days of Program: One
  • Timings: 8:00 AM - 6:30 PM
  • Purpose: The theme of the conference is ‘Put Analytics to work.’ The aim is to create a compelling case of utility for Data Analytics and its various benefits.
  • Number of speakers: 20+
  • Speaker's profile: TBA
  • Whom can you Network with in this Conference: Colleagues and dedicated industry professionals from all over the country. 
  • Registration cost: INR 25,000
  • Who are the major sponsors: Analytics India Magazine

3. Data Workshop and Meetup, Pydata initiative, Mumbai 

  • About the conference: The conference will bring together data scientist and Python experts to discuss improvements in the business. 
  • Event Date: Saturday, May 18, 2019
  • Venue: 91springboard Vikhroli, Opposite Vikhroli Bus Depot, Vikhroli West, Mumbai
  • Days of Program: One
  • Timings: 3:00 PM to 7:00 PM
  • Purpose: Discussion around the scope of architecture implemented by attention and implementation of basic OCR using pytesseract. 
  • Number of speakers: One
  • Speaker's profile: Aatish Patel, Deep Learning Researcher at NanoNets
  • Whom can you Network with in this Conference: Learners and coaches of Python, Deep Learners, Tensorflow and neural networkers. 
  • Registration cost: Free Entry 
  • Who are the major sponsors: NumFOCUS, 91 Spring Board, Rise Mumbai 

4. DataGiri's Code-along Saturdays, Mumbai

  • About the conference: DataGiri's code-along brings incredible opportunity to avail hands-on experience on a wide range of data science skills at an 8-hour workshop. 
  • Event Date: 11th May, 2019
  • Venue: TBA
  • Days of Program: One
  • Timings: 10:00 AM onwards
  • Purpose: A deep look into Data Science by the top Analytics professionals in the industry, followed by an hour-long networking with the leaders in Data Science. 
  • Number of speakers: One 
  • Whom can you Network in this Conference: Professionals and top data scientists 
  • Registration cost: Free Entry

5. Deep Learning with Computer Vision in FinTech, Mumbai

  • About the conference: Deep Learning with Computer Vision in FinTech presents you with a great chance to employ more practises on Deep Learning and Computer Vision. 
  • Event Date: Saturday, May 11, 2019
  • Venue: Rise Mumbai 1902, 19th floor Tower B, Peninsula Business Park, Lower Parel, Mumbai
  • Days of Program: One
  • Timings: 2:30 PM to 5:30 PM
  • Purpose: Learn best practices on Deep Learning and Computer Vision
  • Whom can you Network with in this Conference: Database Administrators, Database Developers, Business Intelligence officials and many other career professionals.
  • Registration cost: Free Entry
  • Who are the major sponsors: GreyAtom and RiseMumbai.

6. Machine Learning - A Graphical Intuition, Mumbai

7. IDF Mumbai Online Meetup, Mumbai

  • About the conference: IDF Mumbai India is a gathering of user experience designers, interaction designers, and information architects.
  • Event Date: May 17, 2019
  • Venue: Online
  • Days of Program: One
  • Timings: TBA
  • Purpose: A session of conversations, meetups, and other engagements with important members of data community.
  • Whom can you Network with in this Conference: Data scientists, specialists and other professionals. 
  • Registration cost: Free Meetup

8. The Fifth Elephant Winter 2019, Mumbai

  • About the conference: The conference will cover data engineering, data governance including data quality, version control and trust in data, and workflows in organizations, leveraging data for business use cases, optimizing analytics with innovations and tooling and inculcating analytical thinking in teams and organizations.
  • Event Date: Friday, 18th Jan, 2019
  • Venue: ISDI ACE, Colab Area, 7th Floor, Tower 2A, One India Bulls Center, Lower Parel, Mumbai, Maharashtra - 400013
  • Days of Program: One
  • Timings: 9:30 AM - 5:20 PM 
  • Number of speakers: Four
  • Speaker's profile: 
    • Kumar Puspesh, CTO & Co-Founder, Moonfrog
    • Govind Pandey, Senior Engineering Manager, Flipkart
    • Kaushik Bhatt, Vice President
    • Vekata Pingali, Co-Founder and CEO, Scribble Data.
  • Whom can you Network in this Conference: Product developers, Data Scientists, Engineers and experts. 
  • Registration cost: Not Disclosed 
  • Sponsors: ISME, Elastic, Trusting Social Engineering, etc.

9. Asian Conference on Recent Advances in Science, Engineering and Technology, Mumbai 

  • About the conference: Asian Conference on Recent Advances in Science, Engineering and Technology is a prestigious event organized to provide an excellent international platform for academicians, researchers, engineers, industrial participants and budding students around the world to SHARE their research findings with the global experts.
  • Event Date: 2 May, 2019
  • Venue: Radisson Mumbai Goregaon, Mumbai, India
  • Days of Program: One
  • Timings: 09:00 AM- 06:00 PM
  • Purpose: The purpose of the event is to provide an opportunity for global participants to share their ideas and experience with professionals expected to join from different parts of the world. 
  • Number of speakers: TBD 
  • Speaker's profile: TBD 
  • Whom can you Network with in this Conference: Academicians, engineers, researchers, budding students and industrial participants. 
  • Registration cost: TBD 

10. India IOT SUMMIT 2019, Mumbai

  • About the conference: IoT aims to establish connections between devices, accessories, people, and events in a smooth way. The summit's objective is to provide models of ease to the consumers. 
  • Event Date: 8th Feb 2019 
  • Venue: Hotel ITC Maratha, Mumbai
  • Days of Program: One
  • Timings: 8:30 AM - 5:00 PM
  • Purpose: Sponsoring or exhibiting at “IOT Summit 2019” is an excellent way to promote your business to a highly targeted group of key decision makers with a specific interest in Iot products and Iot solutions in India.
  • Number of speakers: 30
  • Key Speaker's profile: 
    • Anand Bhangaonka, SVP Head-R&D & SQE - Piaggio Group
    • Achin Sharma, Head, Global IT - Royal Enfield
    • Shaffic Ahamed, VP & Country Manager IT - Sandvik
  • Whom can you Network with in this Conference: Chief Information Officers, Head Engineers, Chief Technology Officers and other career experts.
  • Registration cost: Not Disclosed
  • Sponsors: Ignitarium, Easy Reach, COI - IOT, etc.
S.NoConference nameDateVenue
1.Data Science Congress, 201829/05/2018 - 1/6/2018CIDCO Convention Centre, Mumbai
2.Data Visualisation Summit, MumbaiSeptember 01, 2017The Lalit, Mumbai, Sahar Airport Road, Andheri East, Mumbai.
3.Gartner Data & Analytics, Summit 20176 – 7 June, 2017Renaissance Mumbai Convention Centre Hotel, #2 & 3B, Near Chinmayanand Ashram, Powai, Mumbai
4.India IoT Summit 2017August 22-23, 2017The Lalit, Sahar Airport Rd, Navpada, Marol, Andheri East, Mumbai, Maharashtra

1. Data Science Congress 2018, Mumbai

  • About: The science congress conference highlighted the significance of Big Data, Machine Learning, Cognitive Computing and IoT in organizations. It was an important conference for every budding Data Science patriots and Data Science professionals.
  • Event Date: 29/05/2018 - 1/6/2018 
  • Venue: CIDCO Convention Centre, Mumbai 
  • Days of Program: Three
  • Timings: 9:00AM - 6:00PM
  • Registration cost: INR 5000

2. Data Visualisation Summit, Mumbai

  • About: Unicom organized one-day Event on Data Visualization in Mumbai. It was designed through thorough research by industry experts. It also provided great opportunities to establish interactions between some of the biggest brains of the data industry.
  • Event Date: September 01, 2017
  • Venue: The Lalit, Mumbai, Sahar Airport Road, Andheri East, Mumbai.
  • Days of Program: One
  • Timings: 8:45 AM - 5:15 PM
  • Purpose: The main purpose of this event was to pool together thought leaders, data visualizers, and industry professionals.
  • Speaker Profile:
    • Vasuprad Kanade, Associate Director, Accenture
    • Cyrus Lentin, CEO & CTO, MaexaData
    • Sabrina Shaikh, Anayltics Manager, Maersk
  • Registration cost: INR 9000
  • Who were the major sponsors
    • Datanami
    • Datavail
    • Gerrard Consulting
    • UNICOM   

    3. Gartner Data & Analytics, Summit 2017, Mumbai 

    • About: It was a two-day action-packed conference presenting four tracks of hard-hitting content and developments in the field of data science. 
    • Event Date: 6-7 June 2017
    • Venue: Renaissance Mumbai Convention Centre Hotel, #2 & 3B, Near Chinmayanand Ashram, Powai, Mumbai
    • Days of Program: Two
    • Timings: 8:00 AM onwards
    • Purpose: The purpose was to build and execute data strategy effectively, empower organization and prepare for fast-moving trends such as machine learning and Hadoop.
    • Speaker Profile:
      • Ted Friedman, VP Distinguished Analyst
      • Rita L. Sallam, Research VP and Margaret Heffernan, Entrepreneur, Chief Executive, and Author
    • Registration cost: Standard price: INR 54,500 plus taxes, Public sector price: INR 44,000 plus taxes
    • Who were the major sponsors:
      • Yellowfin
      • Intellicus
      • Qlik 

      4. India IoT Summit 2017, Mumbai

      • About: The India, IoT Summit featured a CEO Panel talk on the establishment of IoT in India and the influx of Big Data and customer experience with IoT.
      • Event Date: August 22-23, 2017
      • Venue: The Lalit, Sahar Airport Rd, Navpada, Marol, Andheri East, Mumbai, Maharashtra
      • Days of Program: Two 
      • Timings: 3:00 PM onwards
      • Purpose: The LTI aimed to showcase its key solutions and offerings across the IoT spectrum. 
      • How many Speakers: Four
      • Speaker Profile:
        • Sudip Mazumder, Deputy Head – Digital, L&T Construction
        • Sachin Vyas, Head – Mosaic Practice, LTI, etc

      Data Scientist Jobs in Mumbai, India

      Below are the steps you should follow to get a job as a Data Scientist.

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

      • Getting started: First step is to choose a programming language. Considering the recent scenario, we suggest you learn Python or R language. 
      • Mathematics and statistics: Data science involves a large number of raw data that are present in the unstructured or raw form. Here mathematics as well as statistics, help to sort out this data and present it in a user-friendly way.
      • Libraries: Data science process involves various tasks ranging from preprocessing the data given to plotting the structured data and finally to applying ML algorithms as well. Some of the famous libraries include Scikit-learn, NumPy, Pandas, ggplot2 and Matplotlib, etc.
      • Data visualization: It’s the job of a data scientist to convert data in such a form that the company can use it. Knowing and presenting the data in a form of graphics or other methods is the ultimate responsibility of a data scientist. The most popular way to visualize data is by creating a graph. Various libraries can be used for this task:
        • Matplotlib – Python
        • Ggplot2 - R
      • Data preprocessing: Unstructured data needs to be processed by the data scientists to make it analysis-ready. Many engineering variables help in doing the processing. This processing is done to convert the data to a structured format so that it can be used in the ML tool for analysis.
      • ML and Deep learning: Having skills in big data, deep learning and machine learning area is a must-have for every data scientist right now. Deep learning is highly-preferred among data scientists as they have to deal with a huge set of data.
      • Polishing skills: Competitions like Kaggle etc. provide an important platform for the budding as well as the established data scientist. Projects and research work also help in polishing the skills, making you better in the league.

       Here is what you need to prepare for a job as a data scientist:

      • Study: Make sure that you have an in-depth knowledge of topics like Probability, statistics, statistical models, machine learning, and neural networks.
      • Meetups and conferences: You need to build your professional network by visiting data science conferences, meetups and tech talks. This will help you with referrals in the future.
      • Competitions: For brushing up your data science skills, you can participate in online competitions like Kaggle.
      • Referral: Referrals are a great source of getting interviews. Your LinkedIn profile needs to be updated and maintained.
      • Interview: Once you feel like you are ready, you can start giving the interviews. You might have to get through a couple of interviews before you land a job. What you need to do is learn from your mistakes and study better for the next interview.

      The major roles and responsibilities of a Data Scientist include the following:

      • Collect the data that is required to meet the needs of the business. This data will be mostly in the unstructured form.
      • After this, you need to extract the relevant data from the huge data and organize it.
      • Next step is creating machine learning tools, techniques and programs for analyzing the data.
      • Lastly, statistical analysis is performed for gathering insights and predicting future outcomes.

      The Data Science career path is as follows:

      Business Intelligence Analyst: A business intelligence analyst is responsible for figuring out how the business works and how the market trends affect it. They perform data analysis to get a clear picture of the current standing of the business.                                                                   

      Data Mining Engineer: A data mining engineer examines the data and creates the algorithm required for data analysis.

      Data Architect: A data architect is responsible for creating blueprints used to integrate, centralize, maintain, and protect the data sources. They work with system designers, developers and users to do the same.

      Data Scientist: A Data Scientist analyzes the data, creates a hypothesis, and explores the patterns in the data. They also develop algorithms and systems that provide insights from raw data.

      Senior Data Scientist: A senior data scientist makes sure that all the future projects, systems and data science are shaped in a way to fulfill the needs of the business.

      Some renowned associations and groups of data scientists are:

      • Mumbai Artificial Intelligence & Deep Learning
      • Data Science community in Thane
      • Mumbai Big Data Analytics Meetup
      • Aegis-IBM Data Science, Big Data, Analytics, AI, ML, DL
      • Love Data Science - Live Data Science
      • Driven by Data - IBM Storage Mumbai
      • Data Science Mumbai
      • Data Science Hub

       Here is how you can network with other data scientists:

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

      It has been seen that the demand for data science jobs has been increased by 15% which was 12% last year in Mumbai. Right now, with huge demand, there are several career options due to organizations like Google, S2 Infotech, BookMyShow, Tata CLiQ, CreditMate, JP Morgan Chase & Co, Truebil, etc, searching for a data scientist in Mumbai:

      • Business Intelligence (BI) Developer
      • Data Architect
      • Applications Architect
      • Infrastructure Architect
      • Data Scientist
      • Machine Learning Scientist
      • Data Scientist
      • Business Analyst
      • Marketing Analyst
      • Data/Analytics Manager

      Here are the tools and software that you need to master to be preferred over other data scientists by the employers:

      • Education: A degree in Data Science will jumpstart your Data Science career. You can also try getting some certifications.
      • Programming: You need to be an expert in programming. Start with the basics and then learn about the data science libraries.
      • Machine Learning: Machine learning and deep learning skills help in creating tools and frameworks required for performing the data analysis.
      • Projects: Take on as many real-world projects as you can. This will improve your data science skills and improve your portfolio.

      Data Science with Python Mumbai, India

      Python is highly preferred by data scientists over other programming languages due to its simplicity and the dedicated packages and libraries made particularly for data science use. It gives data scientists access to a broad range of resources, which helps them solve problems that may come up during the development of a Python program or Data Science model. 

       Here are the 5 most popular programming languages used for Data Science:

      •  R: Although R is difficult to learn, it offers certain advantages that make it perfect to be used in the field of Data Science. Firstly, there are several high quality and open source packages offered by its global community. You can also try data visualization with R using ggplot2. Lastly, it has several statistical functions that help in processing complex matrix operations. 
      • Python: Python is the most commonly used and preferred programming language in the field of data science. It is because it has a syntax that is similar to English language that makes it easy to read, write, and understand. There are several python libraries like Pandas, tensorflow, and scikit-learn that are used for data science projects. It also has the support of its global, open-source community.
      • SQL: You need to have knowledge of SQL to work with databases that includes querying, updating, and manipulating. It also has a very easy syntax.
      • Java: Despite its limited verbosity and libraries, Java is used in Data Science projects because it is a compiled, general purpose, high-performance language which is compatible with several systems. This is because there are already systems in place with its backend coded in Java.
      • 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 how you can download and install Python 3 on Windows:

      • Download and setup: Visit the download page and use the GUI installer to setup Python on your windows. Make sure that while you are installing, you select the checkbox asking to add Python 3.x to PATH. This is your classpath that will allow you the usage of Python's functionalities from the terminal. 

      You can also use Anaconda to install Python. If you want to check if Python is installed, you can try using the following command that will show the current version of Python installed:

      python --version

      • Update and install setuptools and pip: If you want to install and update the crucial libraries, you can use the following command:

      python -m pip install -U pip

      Note: You can create isolated Python environments and pipenv using virtualenv. Pipenv is a Python dependency manager. 

      For installing Python 3 on Mac OS X, you can either simply install the language from their official website using a .dg package or use Homebrew python or its dependencies. Here are the steps you need to follow:

      • Install Xcode: First, you need to install Xcode. You will need the Xcode package of Apple/ Start using the following command: $ xcode-select --install
      • Install brew: Next, you have to install Homebrew which is a package manager for Apple. Start with the following command: 

      /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" Confirm if it is installed by typing: brew doctor

      • Install python 3: Lastly, to install python, use the following command: 

      brew install python

      • If you want to confirm the version of python, use the command: python --version

      You should install virtualenv that will create isolated places for you to run different projects and can even run different versions of Python on different projects. 

      Data Science with Python Certification Course in Mumbai

      Famously dubbed ?the city that never sleeps?, Mumbai scores high among India?s most popular metropolitan areas. With the most prolific film industry in the world, this city has a vibrant and glamorous city life and is a thriving cultural centre, with daily performances in music, dance and drama. Mumbai is a teeming hub of trade and commerce in India. Some of the nation?s most important financial institutions such as Reserve Bank of India, Bombay Stock Exchange, National Stock Exchange are located here. Corporate offices of many national and global companies including Fortune 500 companies, and many foreign banks find their home in Mumbai. The city has thriving markets, business houses and is a melting pot of many different communities reflecting a cosmopolitan lifestyle. Professionals who wish to thrive in their career would find that they can do well here, with certifications such as PRINCE2, PMP, PMI-ACP, CSM, CEH and others. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.

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