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ESC
Data Science
Master Data Science in Python
Course Description
Course Fee
₹ 15,000 incl. GST
Learners
81974
Your Faculty
Dr. Abhinanda Sarkar
PhD Stanford University
Academic Director, Great Learning
Dr. Abhinanda Sarkar has a degree in B.Stat. and M.Stat. from the Indian Statistical Institute (ISI) and a Ph.D. in Statistics from Stanford University. After working in the research teams at MIT and IBM, he spent a decade at General Electric in Bangalore. Currently, he is an Associate Dean at the MYRA School of Business and a certified Master Black Belt in Lean Six Sigma and Design for Six Sigma. He has been currently adjunct or visiting faculty at Stanford, ISI Delhi, the IIM-B and IISC.
Mr. R Vivekanand
MBA (Monash University)
Operations Director, Wilson Consulting Private Limited
Vivek Anand is a data visualization consultant with 10 years of experience. Vivek has an MBA from Monash University Melbourne Vic. Area of specialization includes Marketing & Econometrics. He has worked as Sales & Marketing professional handling teams of leading Indian hospitality brands across the country. His most recent assignment was for India’s largest Luxury hotel by ITC hotels in Chennai. He is a qualified trainer of Tableau 9.0 and has a passion for teaching.
Prof. Mukesh Rao
PGDBA (SIMS)
Professor, Great Learning
Prof. Mukesh Rao is an Adjunct Faculty at Great Lakes for Big Data and Machine Learning. Mukesh has over 20 years of industry experience in Market Research, Project Management, and Data Science.
Mukesh has conducted over 100 corporate trainings. Data Science training covers all the stages of CRISP DM, tools and techniques used in each stage, machine learning algorithms and their application. Big Data training covers core Apache Hadoop technologies including HDFS, YARN, Map Reduce, PIG, HIVE, SQOOP, FLUME, SPARK and MongoDB.
Student Testimonials
Suraj Suresh
Data Scientist
DXC Technology
Naga Pavan
Consultant Analyst
Kantar
Sonakshi Pattnaik
Data Scientist
Genpact
Target Job Roles
- The course helps you in gearing yourselves for the below-mentioned job roles which are in high demand and pay a good salary
- Data Scientist
- Data Analyst
Projects
Learn practical skills by working on real-world projects. As part of this course, you will be working on the below mentioned projects
Campaign for selling personal loans
Certificate
Earn a Verified Certificate from Great Learning Academy, which is highly regarded and valued by corporates. Make your CV & LinkedIn profile stand out from the rest.
Curriculum
Module 1
Python for Data Science
2.5 hours
- Numpy
- Pandas
- Matplotlib
Module 2
Visualization with Tableau
1.5 hours
- Tableau Desktop Exploration
- Data Representation and Analysis using Various Plots and Charts
Module 3
Introduction to Statistics
1 hours
- Probability and Probability Distributions
- Introduction to Statistics
- Normal Distribution
- Hypothesis Testing
- Show more
Module 4
Exploratory Data Analysis
1.5 hour
- Introduction to EDA
- Measure of Spread or Dispersion
- Method of Central Tendency
- Correlation
Module 5
Supervised Learning
5 hours
- Linear Regression
- Logistic Regression
- KNN
Module 6
Unsupervised Learning
1.5 hours
- Introduction to Clustering
- K means Clustering
FAQs
For how long can I access these courses?
You can access all these courses for 1 year.
Is it 100% online learning and self-paced?
It is a 100% online learning course that you can learn at your own pace through our website and mobile app.
On what basis are the certificates rolled out?
The certificates are rolled out as and when you complete the mandatory course content and submit the project with a satisfactory grade.
Will I gain access to any sort of Forum support?
Yes. You will gain complete access to our Discussion forum support in your course to connect with our SMEs to resolve your course content related queries within 48 hrs.
Is there a refund in case I want to discontinue from the course?
Fees once paid is not refundable. Kindly make an informed decision before enrolling into the course.
Python is an object-oriented high-level programming language which is currently the most preferred programming language by professionals as well as organisations. Most of the beginners prefer python as their first programming language as it is simple and versatile. One can use Python for data analysis, web development, Internet of Things and many other areas of business and technology. Python is also well-supported by the community and keeps up with its popularity with new developments.
Why choose Python?
Python has become the most preferred language for enabling data science and machine learning applications. It has a lot of advantages over other languages as it is swift and the syntax is easy too. As it is a java-based programming language, one can extend its applications in the field of data science beyond analytical and statistical modelling. It will also simplify developing web applications and integrate them directly with the analytical models in the background.
Another advantage of using Python is that one can easily integrate with other platforms and programming languages. Programmers find it extremely easy to transition to the analytics and data science domains as Python has a common object-oriented programming architecture. It also has excellent documentation support.
Here are seven reasons why Python should be your top choice among programming languages:
- The code is easy to read and maintain
- Multiple programming paradigms
- Its compatibility with major platforms and systems
- A robust standard library
- Simplified software development
- Open-source framework and a long list of tools
- Test-driven development
Must-have skills for mastering Python for Data Science
Mastering Python will involve learning the basics, an understanding of Python libraries and their functionalities, and practice. Here are the must-have skills for a programmer to become an expert in Python.
- Familiarity with Object Relational Mapper (ORM) libraries
- Knowledge of Python web frameworks such as Django and Flask
- Ability to integrate multiple data sources and databases into a single system
- Knowledge of front end technologies such as JavaScript, HTML5, CSS3 is an added advantage
- Understanding fundamental design principles behind a scalable application
- Event-driven programming
- Unit testing and debugging skills
- Good analytical and problem-solving skills
- Familiarity with Python Packages such as Numpy, Scikit learn
Most Common Python Libraries for Data Science
If you wish to learn Python for data science, then it is very important to have a knowledge of Python libraries that are used in data science applications. Some of the commonly used libraries for data science are:
- Matplotlib: Matplotlib is a plotting and visualisation library used for generating graphs with analysed data. By putting together some simple lines of code, it helps you plot some of your most complex graphs. Pyplot is the most widely used module of Matplotlib. It is open-source and has a MATLAB-like interface and is also a good alternative to MATLAB’s graphics module. Matpltlib’s utility and popularity can be gauged by the fact that NASA’s data visualisations of Phoenix Spacecraft’s landing were illustrated with Matplotlib.
- NLTK: Natural Language Processing Toolkit (NLTK) is a collection of libraries that helps in building statistical models that helps machines understand human language.
- Scrapy: A collaborative framework for extracting the data required from websites. It is a simple and fast tool.
- BeautifulSoup: It is another library used for web-scraping, i.e., extracting and collecting information from websites.
- StatsModels: It is a Python library that offers multiple opportunities, such as performing statistical tests, statistical model analysis and estimation, and more. It also has a function for statistical analysis to achieve high-performance outcomes while processing large statistical data sets.
- XGBoost: This library provides high-performance implementation of gradient boosted decision trees. XGBoost is flexible, efficient, and portable and provides highly optimised, scalable, and fast implementation of gradient boosting.
- Plotly: It helps in plotting graphs easily and works really well in interactive web applications. Various types of basic charts such as line, pie, scatter. Heat maps, polar plots, etc. can be created with the help of Plotly. Whatever visualisations data science professionals can think of can be plotted on a graph using Plotly.
- Pydot: Used for generating complex oriented and non-oriented graphs. It is specially used while developing algorithms based on neural networks and decision trees.
- Gensim: It is used to extract the underlying topics from a large volume of text for topic modelling, document indexing, and more. It has the ability to handle large text files without loading the entire file in memory.
- PyOD: It detects outliers in multivariate data providing access to a wide range of outlier detection algorithms. Outlier detection is also known as anomaly detection and refers to identifying rare events, observations, or items in a data set which are different from the general distribution of a population
Other libraries that are popular in Data Science domain are:
- Numpy
- Scipy
- Pandas
- Seaborn
- Bokeh
- Scikit-Learn
- Tensorflow
- Keras
- Pytorch
- Theano
How to Learn Python for Data Science?
Python’s popularity mainly comes from the ease of learning the language. It is simple and type-free and therefore it is easy to learn. Many people ask how much time does it take to learn Python? The answer is that the time taken to learn the language will depend on the level of expertise you want to achieve with the language. Also, depending on individual ability, the learning curve could be shorter or longer. Overall, learning Python is not too difficult and absolutely doable even if one is not from a technical background.
The highly sophisticated skills important for data science include concepts of data analytics, experience with required libraries, image processing and more. Let us see how one should proceed with learning Python.
Start with the basics of Python, such as syntax, keywords, functions, classes, data-types, basic coding and exception handling. Then depending on the nature of your work, you can learn specific skills such as synchronisation techniques, database programming, multithreading etc.
Higher-level skills in Python include data analytics, image processing, hands-on experience of various libraries such as Numpy and Pandas, and more. To master these skills, it will take implementation, experimenting, and practice.
Python knowledge for a Data Scientist
A data scientist should possess knowledge about data analysis, data interpretation, and data manipulation with Python. Mathematics and statistics are also very beneficial for making the right decisions. Adequate knowledge of Python libraries like Tensorflow, Scikit learn and others mentioned above is also crucial for successfully fulfilling the role of a data scientist. Some of the machine learning algorithms such as Naive Bayes, regression analysis, etc. are essential as well.
About the Program: Data Science for Python
Sign up for this program to learn Python for Data Science. The course curriculum covers all the important topics within 15 hours of quality content delivered by top faculty Dr Abhinanda Sarkar, Mr R Vivekanada, and Prof Mukesh Rao.
This program is also useful if you want to learn Python for Data Analysis along with mastering Python for Data Science. If you have read Python for data science for dummies, this course will seem more relevant and a step ahead to it. With a detailed intro to Python for Data Science, the course covers all python basics and is suitable for professionals from a non-coding background.
The course curriculum is conducive to learning and covers a wide range of topics starting from the basics. You will learn modules such as Numpy, Pandas, and Matplotlib. Exploratory data analysis concepts such as Data Cleaning, Data Preprocessing, and Feature Engineering are also covered. Further, you get to learn how to apply Python in Supervised and Unsupervised learning. Lastly, you get to learn data visualization with Tableau for data representation and analysis using plots, charts, and other visual elements.
With the assignments and practice tests that come along with the course, you can ensure that you practice what you learn and master the concepts. You will also get a direction on how to apply your learnings practically into your job.