Data Science with Python Training in Delhi, 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.

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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 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.

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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.

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

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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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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

Vito Dapice

Vito Dapice

Data Quality Manager

5/5

The trainer was really helpful and completed the syllabus on time and also provided live examples which helped me to remember the concepts. Now, I am in the process of completing the certification. Overall good View More

Attended PMP® Certification workshop in April 2020

Astrid Corduas

Astrid Corduas

Telecommunications Specialist

5/5

The instructor was very knowledgeable, the course was structured very well. I would like to sincerely thank the customer support team for extending their support at every step. They were always ready to help an View More

Attended Agile and Scrum workshop in June 2020

Raina Moura

Raina Moura

Network Administrator.

5/5

I would like to extend my appreciation for the support given throughout the training. My special thanks to the trainer for his dedication, and leading us through a difficult topic. KnowledgeHut is a great place View More

Attended Agile and Scrum workshop in January 2020

Barton Fonseka

Barton Fonseka

Information Security Analyst.

5/5

This is a great course to invest in. The trainers are experienced, conduct the sessions with enthusiasm and ensure that participants are well prepared for the industry. I would like to thank my trainer for his View More

Attended PMP® Certification workshop in July 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

Anabel Bavaro

Anabel Bavaro

Senior Engineer

5/5

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

Alexandr Waldroop

Alexandr Waldroop

Data Architect.

5/5

The workshop held at KnowledgeHut last week was very interesting. I have never come across such workshops in my career. The course materials were designed very well with all the instructions were precise and co View More

Attended Certified ScrumMaster (CSM)® workshop in January 2020

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Data Science with Python

What is Data Science?

Today, the virtual world has transformed into real in too many ways to count. From cloud kitchens to real estate every business has an online presence; generating millions of data every single day. At the same time companies need data to estimate and decide on the future of a company. The work of a data scientist is to understand and codify data that will enable an organization to make comprehensive choices for their company. In such a situation, the demand for data scientists with excellent grasp of the medium becomes a necessary factor. 

Delhi is not just any city, it is the national capital of India and is home to some of the most prestigious universities and leading companies in the field of data science such as Amazon, Crescendo, ZS, Michael Page, Engineer.ai, Emaar, Hike, Cube26 etc. 

There are other factors that play an important role for data science becoming a popular career choice. They are:

  • The decision making of companies is highly data-driven.
  • With the demand for professional data scientists not being fulfilled by the limited number of data scientists out there, the companies are paying a high salary to data scientists. 
  • Since data is being generated in high quantity, companies are shifting to data based decision making by using the raw data that is at their disposal. 

This leads to increased need for data scientists in every sector and makes data science a coveted career choice for employees.

Technical skills are essential for working in data science. The good news is Delhi offers you a lot of scope in this space because it is home to some of the elite universities such as Indian School of Business and Finance, Indian Institute of Technology Delhi, Jawaharlal Nehru University, University of Delhi, Department of Computer Science, Indraprastha Institute of Information Technology, etc. Since, the work of a data scientist is to classify, process and analyze data they would need basic technical skills to adequately help a company make the best of the raw data available to them. 

Following are the main technical skills that are a must for anyone considering a job as a data scientist:

  1. Python Coding: This is the most comprehensive and popularly used programming language. Python allows data scientists to create datasets and perform various operations on data sets.
  2. R Programming: This is a variant of Python programming. Programming languages enable data scientists to understand and find patterns in raw data; making it essential to learn at least one programming language. 
  3. Hadoop Platform: While not an absolute necessity, Hadoop platform is a preferred skill for lot of data science projects. 
  4. SQL database and coding: SQL is a platform that helps data scientists to access, communicate and work with data. With MySQL data scientists can perform various operations on data easily without having technical skills.
  5. Machine Learning and Artificial Intelligence: The potential Machine Learning and Artificial Intelligence skills required by data scientists are as follows:
    1. Reinforcement Learning
    2. Neural Network
    3. Adversarial learning 
    4. Decision trees
    5. Machine Learning algorithms
    6. Logistic regression etc.
  6. Apache Spark: This is the most popular data sharing technology worldwide. This helps data science algorithms to run faster. Apache also helps in organizing and dissemination of data as well as handling complex unstructured data sets.
  7. Data visualization: Data visualization tools like d3.js, Tableau, ggplot and matplotlib helps in processing and formatting complex data sets for easy comprehension. This enables organizations to directly work with data.

Unstructured data: Data Scientists have to work with unstructured data which are not labeled or classified into database values. These include videos, social media posts, audio samples, customer reviews, blog posts etc.

Technical knowledge is not the only factor that determines the credibility of a Data Scientist. There are other factors that play a major role in how successful one will be in securing a Data Scientist job.

Asking ‘why’: Being continually curious is an important quality to have in a data scientist as he/she will work with a large amount of data.

Clarity: Having a clear idea of why you are working with a particular data set and what can be achieved from working on it will determine your quality as a data scientist.

Creativity: Data science is all about having a drive to make your work environment efficient. Thus having the creativity to constantly reinvent methods of processing and analyzing data will be an added advantage.

Questioning judgments: There is always a possibility of going overboard with one's creativity and questioning what can and cannot work is the prerogative of the data scientist.

Amazon, Crescendo, ZS, Michael Page, Engineer.ai, Emaar, Hike, Cube26 are some of the prominent companies operating in New Delhi. These companies make it beneficial for an aspiring data science professional to reside in the city. When more than half of the world’s population is using something you are an expert in, there will be certain benefits to it.

  1. Highest paying job: Qualifying as a certified data scientist needs a lot of training and hard work, thus the pay is equally handsome. The average salary of a data scientist in Delhi is ₹ 10,02,509/year which is 13% higher than the other cities.
  1. Great bonuses: Apart from the salary, data scientists get huge bonuses including equity shares and signing perks.
  1. Privilege of becoming an educator: Becoming a data scientist requires a lot of knowledge. Thus by the time you become an expert you will probably have a Master’s or a PhD which will lead you to receive offers to become a lecturer or a researcher at governmental as well as private institutions.
  1. Mobility: One of the greatest perks of being a data scientist is the freedom to work wherever, whenever.
  1. Networking: Being involved in the tech world by publishing research papers in international journals, attending conferences will expand your interaction with people in the industry. You can get referrals from such networks.

  1. Security: Everyday there are new technologies coming up and disappearing without making any significant mark. This is not the case with data science.

Qualifications and Skill Sets of Data Scientists

  1. Analytic problem solving: To find a solution, one needs to have an analytical mind to understand the problem. In order to do that one needs to be aware of all the strategies and have a clear perspective to reach the right solution. 
  2. Communication Skills: Collecting data and analyzing it is not the only responsibility of a data scientist. Unless you can communicate the customer analytics or business strategies to companies then your job is only half done.
  3. Industry knowledge: This is of great value if you want to be ahead of your competitors. Being up to date with the happenings in the industry will help you understand what needs your attention and what you can discard. Being aware of what your global competitors are thinking and adapting them in your work will make you an asset in any company; bringing new opportunities. 

While you may become an expert in Data science, it is always preferred that you are up to date with the new developments in data science. For that you need to attend:

  • Bootcamps: Bootcamps are the best way to improve your Python programming skills. Bootcamps are held for 1 to 2 weeks or for 4-6 months, offering both theoretical knowledge as well as hands on experience.
  • MOOC courses: These are virtual courses and provide excellent knowledge of latest trends in the industry. These courses are taught by experts helping you refine your implementation skills through assignments.
  • Projects: Projects are a great way to work on new solutions to already worked out problems depending on the restrictions of the projects. More you work on projects, the better your analytical and problem solving skills will become.
  • Competitions: Attending competitions like Iron Viz or Kaggle, etc, improves your problem solving skills while giving you an idea of where you stand in relation to your peers.

Data Science can be really grasped through constant practice and keeping yourself updated with every new programming and preprocessing or analytic skills. Even after securing a job one should continue working on individual projects and enter competitions to brush up as well as have fun with the skills of data science that might ignite your creative capacity.

Anything that gives insight to customer preferences is data. From your hospital prescription, stock investments, browsing history, or favorite color everything is data and can be used by companies to make ideal products, improving customer experience. Some of the major companies hiring skilled data scientists in Delhi are Amazon, Crescendo, ZS, Michael Page, Engineer.ai, Emaar, Hike, Cube26 etc. 

The best way to master any technique is through practice and to master data science, the best approach would be to work through problems while solving data science algorithms. There are few data science problems which can be worked on to increase your skills in data science. The data science problems have different difficulty levels, making it easier for aspiring data scientists to choose the dataset problems they would prefer to work with according to their experience level. 

The beginner level datasets can be easily solved by anyone who has basic ideas of mathematics and statistics. The intermediate level has regression problems which need some idea of coding which helps working with the large amount that consist the datasets. The advanced level requires an increased experience of the different aspects of data science to work quickly on the datasets which require intuitive and technical skills.  Apart from getting experience, working on datasets is really interesting and fun to do. This makes it a positive experience without being bored. One can get different difficulty level datasets to work on from Analytics Vidhya website.

How to Become a Data Scientist in Delhi, India

Due to the fact that Delhi is home to some of the best institutions in the country as well has some of the leading companies situated here, it makes living in Delhi highly beneficial for an aspiring data scientist.

The following points will guide you to become a successful data scientist,

  1. Acquire basic programming skills: One of the first steps towards becoming a data scientist is to learn a programming language. Either Python or R programming are the most common ones that one can master.
  2. Mathematics and statistics: As data science deals with data, it is important to have basic skills of algebra and statistics.
  3. Data visualization: The work of a data scientist is not just to understand data themselves but make it simple and coherent so that non-experts can understand it perfectly. Visualization of data becomes an important aspect of data science as it is the end user who needs to understand the data generated more than the scientific aspect of data analysis. Having the ability to visualize patterns and common qualities will help the analyst to make sense of the data produced. 
  4. Deep Learning and ML: Having knowledge of deep learning and ML are a must for any data scientist. It is through the skills of deep learning and ML that data scientists analyze the data provided.

Some of the most successful companies in the world rely on data science for their business growth.  Below, listed, are the skill sets and steps you should take to become a data scientist-

  1. Get a degree: Data scientists are mostly Master’s or PhD degree holders. Hence, it is important to start preparing, reading and practicing as early as you can. You could get into numerous programs online or offline, or get yourself a degree on basics of mathematics and algebra.
  2. Handling large quantity of data: Handling unstructured data is essentially the job of a data scientist. How to categorize the infinite number of data getting stored and made cohesive is the most important responsibility as it entails a lot of complexity. Working on data sets and projects can improve one’s eye for useful data. 
  3. Software and techniques to master: The softwares like Python, R and Hadoop are important tools and over 53% data scientists are fluent in both R and Python programming.  Being accustomed to using these will kick-start your data science career:

IMS, IIT, Indraprastha Institute of Information Technology, etc are some of the globally recognized institutions which offer data science courses. The good news is that all of them are located in Delhi. This is highly beneficial for students who are preparing for a data science job.

  • Networking: Interacting with your peer group will increase your conceptual clarity and you will find networking opportunities.
  • Structured learning: Having a schedule for your curriculum will not only provide a holistic idea about the discipline, but will also help in maintaining timelines and being more productive.
  • Internships: Getting a hands-on experience by doing internships can be very helpful and provide you enough experience for the job.

Delhi is home to Indian School of Business and Finance, Indian Institute of Technology Delhi, Jawaharlal Nehru University, University of Delhi, etc. and these institutes are some of the best universities which offer advanced courses in the field of data science. The need for a master’s degree in Data Science depends on the degree one has pursued before. The necessity of a Master’s degree depends on the following points mentioned below. Score yourself according to the factors mentioned, if you score more than 6 points it is advisable that you get done with a master’s degree.

  • You have a strong STEM (Science/Technology/Engineering/Management) background: 0 points.
  • You have a weak STEM background (Biochemistry/Biology/Economics or other such degrees): 2 points. 
  • You come from a non-STEM background: 5 points
  • You have less than 1 year experience of working with Python programming: 3 points
  • You have never had a job which required you to code on a regular basis: 3 points
  • You feel you are not good at independent learning: 4points
  • You do not understand when it is said that this scorecard is a regression algorithm: 1 point.

Programming is at the heart of data science and is an absolute must for anyone to learn in order to become a Data Scientist. It is not just the most essential aspect in Delhi but all over the world. If you want to become a data scientist, this is the first step which you must cross. The other skills are as follows:

Data sets: A job of a data scientist revolves around the analysis of a large number of data sets. Knowledge of programming is required to help you analyze those data sets. 

Statistics: The ability to program goes hand in hand with your ability to use statistics. As you start working on programming, a lot of statistical techniques will be needed to make it easier for you to write code and create new statistical methods. 

Framework: Having programming ability improves your efficiency and ability to structure the  data. It is important that data scientists create frameworks for analyzing data so that visualization, interpretation and data pipeline are created, which will allow selected individuals to access the data at any time. Working with millions of data requires having a foolproof structure for storage of data and preventing it from being breached. 

Making the work space efficient and secure is the ultimate responsibility of a data scientist.

Data Scientist Salary in Delhi, India

In Delhi, a Data Scientist can earn up to Rs. 9,92,129 per year.

Delhi offers an annual salary of Rs. 9,92,129 per year as compared to Rs. 7,50,000 offered in Kolkata.

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

The average annual earnings of a Data Scientist in Delhi is Rs. 9,92,129 as compared to Rs. 6,15,496 earned by a Data Scientist in Bangalore.

The demand for Data Scientist far outweighs the supply. With all major, mid-sized and small-sized firms trying their hands on data science, the demand for Data Scientists in Delhi has only increased.

The benefit of being a Data Scientist in Delhi is that you can get an opportunity to work with all the major tech companies like Accenture, Deloitte, etc. 

Delhi, the capital of India, is a hub for the tech companies. It is cheaper than other major tech cities like Bangalore. There are tons of conferences, meetups, and summits organized in the city for data scientists to attend. Also, Delhi is connected to Noida and Gurgaon that increases your job opportunities. As you have a key role in deciphering useful insights from raw data, it helps Data Scientists to get in touch with top-level executives. Also, being trained in data science gives you the freedom to work in any field that you want.

The major companies hiring Data Scientists in Delhi are IPSOS, Vehere, Global Analytics, Capillary, Accenture, IBM Research, Opera Solutions, etc.

Data Science Conferences in Delhi, India

S.NoConference nameDateVenue
1.PyData Delhi Meetup #31, Delhi, IndiaSaturday, May 11, 2019UiPath Academy, Golf Course Road · Gurugram
2.International Conference On Signal Processing And Big Data Analysis (ICSPBA-19), Delhi, 201915th May, 2019Barakhamba Avenue, Connaught Place, Near Modern School, New Delhi, Delhi 110001

1. PyData Delhi Meetup #31, Delhi

  • About the conference: The 31st PyData Delhi Meetup is an incredible opportunity to discuss and strengthen data science and its further developments. 
  • Event Date: Saturday, May 11, 2019
  • Venue: UiPath Academy, Golf Course Road, Gurugram
  • Days of Program: One 
  • Timings: 2:00 PM to 6:00 PM
  • Purpose: Pydata brings together a group of enthusiasts who are inclined in building intelligent systems, elastic, interactive analytics capabilities and graph databases. 
  • Number of speakers: TBA
  • Speaker's profile: TBA
  • Whom can you Network with in this Conference: Career professionals, social media managers and dedicated data scientists. 
  • Registration cost: Free Entry 
  • Who are the major sponsors: NumFOCUS, Hike, Microsoft, SocialCops, etc.

 2. International Conference On Signal Processing And Big Data Analysis (ICSPBA-19), Delhi

  • About the conference: ICSPBA-19, organised by Science Society-Japan  provides an opportunity to research delegates, scholars, and students to share developments and experiences in their fields.
  • Event Date: 15th May, 2019
  • Venue: Barakhamba Avenue, Connaught Place, Near Modern School, New Delhi, Delhi 110001
  • Days of Program: One
  • Timings: TBA
  • Number of speakers: TBA
  • Speaker's profile: TBA
  • Whom can you Network in this Conference: Industrial professionals, Data analysts and experts from all over the country.
  • Registration cost:    
    • Student (B.Tech/B.E) - INR 5500
    • Student(M-Tech) - INR 6500
    • PhD/Research Scholar - INR 6500
    • Academian - INR 7500 
    • Listeners - INR 2500
S.NoConference nameDateVenue
1.PyData Delhi 2017

2-3 September, 2017

Indraprastha Institute of Information Technology Delhi, Shyam Nagar, Okhla Industrial Area
2.International Data Science Summit, 201819th February, 2018
India Habitat Centre, Lodhi Road, Near Airforce Bal Bharati School, Institutional Area, Lodi Colony
3.Data Science All Heads
July 21, 2018
CoWrks Tower A, Paras Twin Towers, Golf Course Road, Sector 54, Gurugram
4.Developer Connect, Delhi
September 25, 2018
Hyatt Regency, Bhikaji Cama Place, Ring Road, New Delhi-110066

1. PyData Delhi 2017, Delhi

  • About: Pydata looked for papers and proposals on data science, Deep learning, Machine learning, Development of Python and Julia and AI.
  • Event Date: 2-3 September 2017
  • Venue: Indraprastha Institute of Information Technology Delhi, Shyam Nagar, Okhla Industrial Area
  • Days of Program: Two 
  • Timings: 08:00 AM - 06:00 PM
  • Purpose: The conference assembled the developers of data analysis to share ideas and learn from each other. It is a global community which works together to discuss how to make the best use of the available Python tools. 
  • How many Speakers: Five
  • Speaker Profiles: 
    • Anuj Gupta, Senior ML researcher
    • Ponnurangam Kumaraguru, Associate Professor of Computer Science 
    • Farhat Habib, Senior Research Scientist
    • Prabhu Ramachandran, Member of the Department of Aerospace Engineering, IIT Bombay
  • Registration cost: INR 1000
  • Who were the major sponsors:    
    • Anaconda
    • Platinum Python
    • Jet Brains
    • Xebia.

    2. International Data Science Summit, 2018, Delhi

    • About: The Summit aimed to provide unique research into establishing a data-driven phenomenon in organizations, and the perks of influxing data and analytics to carry out the decision-making process.
    • Event Date: 19th February, 2018 
    • Venue: India Habitat Centre, Lodhi Road, Near Airforce Bal Bharati School, Institutional Area, Lodi Colony
    • Days of Program: One
    • Timings: 9:00 AM - 6:00 PM
    • Purpose: Internationally acclaimed researchers and scientists came together to share innovative ideas on how to efficiently and correctly obtain the research & insights from data.
    • Speaker Profile:
      • Christopher Arnold, Senior Director, Wells Fargo 
      • Wael William Diab, Senior Director, Huawei Technologies 
      • Ujjyaini Mitra, Head Analytics, Viacom18
    • Who were the major sponsors:    
      • Tata Cliq
      • DBA
      • Times Internet

      3. Data Science All Heads, Delhi

      • About: The conference entailed discussions about the changing organizations and how people think and operate. Further, there were sessions about how the data keeps doubling itself every couple of years. 
      • Event Date: July 21, 2018
      • Venue: CoWrks Tower A, Paras Twin Towers, Golf Course Road, Sector 54, Gurugram
      • Days of Program: One
      • Timings: 9:30 AM - 1:30 PM
      • Purpose: The conference aimed to cover an introduction to the internals, including enterprise use cases, distributed machine learning and its leverage on data companies. 
      • How many Speakers: Five
      • Speaker Profile:
        • Rangasayee Chandrasekaran, Senior Product Manager, Qubole 
        • Jaidev Deshpande, Practice Lead - Data Science at Juxt Smart Mandate 
        • Somya Kumar, Software Engineer, Qubole
      • Registration cost: Free Entry
      • Who were the major sponsors:   
        • Qubole Analytics Vidhya

        4. Developer Connect, Delhi

        • About: Developer Connect connected transforming India with deep learning, hence enabled the Einsteins and Da Vincis of Our Era.
        • Event Date: September 25, 2018
        • Venue: Hyatt Regency, Bhikaji Cama Place, Ring Road, New Delhi-110066
        • Days of Program: One
        • Timings: 8:00 AM - 4:30 PM
        • Purpose: The conference converged social transformations, technology leaps and genuine economic needs of the coming decade, lifting AI from academics and pushing it to the frontlines of the business. 
        • Registration cost: Free Entry
        • Who are the major sponsors: 
          • Nvidia

        Data Scientist Jobs in Delhi, India

        The ideal path to securing a job as a data scientist is as follows:

        • Getting started early
        • Have a good grasp of mathematical concepts
        • Having the skills to work with data visualization libraries
        • Skills of data visualization
        • Data processing
        • Machine Learning and deep learning
        • Natural language processing
        • Polishing skills

        To prepare to take an interview as a Data Scientist, the following ways might help you prepare well.

        Study: Reread whatever you have learnt till now. There are few things you could brush up on:

          • Probability
          • Statistics
          • Statistical models
          • Machine Learning
          • Understanding of neural networks.
        • Meetups and Conferences: Going to tech summits or developer meetups will connect you with the people who could one day become your colleague. This is a good way to do some networking.
        • Competitions: Competitions are the best platforms to test your skills. Taking up projects to work on from Kaggle or GitHub would help polish your skills.
        • Referral: Having good referrals is considered one of the most important parts of a job interview. You should always keep your LinkedIn profile updated. 
        • Interview: Once you feel that you are ready for taking an interview, take one. Be comfortable and learn from your experience. Think of where you went wrong and how you could have answered the question that you were not prepared for during the interview. 

        Making data easy to infer from is the job of a data scientist. Below are some other Roles and Responsibilities of a Data Scientist:

        1. Classifying structured and unstructured data through pattern recognition and creating database.
        2. Finding data that is relevant to the business and can be profitable from among the vast number of data.
        3. Develop Machine Learning technologies, programs and tools which will make accurate analysis of the data.
        4. Statistical analysis of appropriate data for predicting future developments of a company is also expected of a data scientist.

        The average salary for a Data Scientist is ₹ 10,02,509 per year in Delhi, which is 13% above the national average. 

        A data scientist not only analyzes data but finds the relevant ones and directs the future of a company by predicting future outcomes. Thus there are various roles and responsibilities of a data scientist that are a part of a data scientist’s career graph:

        • Business Intelligence Analyst: Anyone in this position is expected to analyze the available data to understand the business and marketing trends of the industry his/her company is part of.
        • Data Mining Engineer: An engineer in data science has the task of analyzing data for the company as well as other third parties. Not only that, engineers are expected to optimize data analysis process by developing sophisticated algorithms.
        • Data Architect: A Data Architect’s work is to make the data sources more approachable. He/She works alongside developers, system designers to integrate and protect data while finding ways of centralizing it making it more accessible.
        • Data Scientist: The data scientist works as an interpreter and idea creator by working with sets of data that correspond with particular business ventures and predicts the efficacy of it by developing hypothesis and comparing similar data.
        • Senior Data Scientist: Senior data scientist is expected to work with data in order to predict the future of a company. He/She should create projects and develop systems in the present with an eye towards the future so that the future conditions of a company can be predicted.
        • Delhi Data Science #ODSC
        • StepUp Analytics Delhi. Learn Data Science
        • Big Data Group - Delhi/NCR
        • Artificial Intelligence and Data Analytics Group(AIDAG)
        • Future of Data: New Delhi
        • Data Science & Machine Learning Meetup in Delhi

        There are various ways one can look for possible employees:

        1. Through Data Science conference
        2. Online platforms like LinkedIn
        3. Social gatherings like Meetup

        Being the most popular career choice of 2019 there are various career opportunities for a Data Scientist-

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

        Amazon, Crescendo, ZS, Michael Page, Engineer.ai, Emaar, Hike, Cube26, etc are either directly based or have a branch in New Delhi and are constantly in search of skilled data scientists. Below are the key points on which every data scientist is evaluated for being considered as a potential employee.

        1. Education: According to the Burtch Works Studies, most data scientists have an advanced degree, either a master’s or Ph.D. Most entry- level data scientist jobs also require at least a bachelor level degree in data science or a closely related field. You can also enrol for certification courses to get recognized academic qualifications for your résumé.
        2. Programming: Anyone who works with data these days needs to be well versed with R and Python programming languages.
        3. Machine Learning: Machine Learning is essential for any data science project as it is ML that helps analyze data to identify patterns and trends. Theoretical research or adopting a theory to practice also requires a deep knowledge of ML. 
        4. Projects: Make sure to walk through real-world use-cases as most companies prefer data scientists with some hands-on experience. 

        Data Science with Python Delhi, India

        • Python is the most simple and readable programming language that instantly attracts data scientists. It comes with appropriate analytic libraries and tools that are ideal for the kind of work done in data science.
        • The diversity of resources available on Python makes it a safe option for data scientists.
        • Another advantage of using Python is the availability of a community of developers using the same programming language. Python being the most popular programming language, the number of people working on it is high.

        Data Science is a vast field which requires working with a large number of libraries. Finding the right programming language to master is, therefore, important for efficient working with all the libraries-

        R programming: The only challenge of R is its steep learning curve, but it is an important language for various reasons

        • It has a huge open-source community that provides numerous high quality open-source packages for R.
        • It boasts of smooth handling of matrix operations and has large statistical functions.
        • It has ggplot2 that enables data visualization

        Python: With lesser packages than R, Python is still considered to be popular with data scientists. The reasons for that is-

        • Libraries like pandas, scikit-learn and tensorflow equip Python to provide most library needs for data science purposes.
        • It is very easy to use and operate.
        • It has an open-source community that is considered one of the largest one.

        SQL: Working on relational databases, Structured Query Language has-

        • Readable syntax
        • Efficiency in updating, manipulating and querying data for relational databases.

        Java: One of the oldest programming languages, Java has limited libraries limiting its potential. Nevertheless it has some advantages.

        • Systems coded with Java at the backend makes it easier to integrate data science projects with it making it a compatible option.
        • It is a high performance, general purpose, compiled language.

        Scala: Working on JVM, it is considered rather complicated. But it does have some advantages:

        • Running on JVM, Scala can run on Java as well.
        • Used alongside Apache Spark it enables high performance computing cluster.

        The following are the steps to downloading Python 3 for Windows:

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

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

        python --version

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

        python -m pip install -U pip

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

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

        • Install xcode: To install brew, you need Apple’s Xcode package, so start with the following command and follow through it: $ xcode-select --install
        • Install brew: Install Homebrew, a package manager for Apple, using the following command: 

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

        • Install python 3: To install the latest version of python, use: 

         brew install python

        • To confirm its version, use: python --version

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

        Data Science with Python Certification Course in Delhi

        Delhi is the national capital of India, and is a city that connects two different worlds. Old Delhi, once the capital of Islamic India, is a maze of narrow lanes lined with collapsing havelis and formidable mosques. In contrast, the imperial city of New Delhi built by the British Raj comprises spacious, tree-lined avenues and imposing government structures. Delhi has been the seat of power for several rulers and many empires for about a millennium. The city is known for its captivating ancient monuments, fascinating museums and art galleries, architectural wonders, a vivacious performing-arts scene, fabulous eating places and bustling markets. As the political hub of India, every political activity in the country traces its roots here. With government offices and leading enterprises based in Delhi, the city offers great prospects for IT professionals in IT Security, Information Technology and many other areas. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.

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