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Python Data Science Course - Data Science using Python

Best Data Science certification course to learn data science and machine learning using Python!

Job oriented Data Science certification course to learn data science and machine learning using Python! Python which once was considered as general programming language has emerged as a star of the Data Science world in recent years, owing to the flexibility it offers for end to end enterprise wide analytics implementation. This data science training covers data handling, visualisation, statistical modelling and machine learning effectively with practical examples and case studies making it one of the most practical Python online training. Even if you are looking for live Data Science oriented Python training in Bangalore or Delhi NCR this is just the right course.


If you are looking for a comprehensive Data Scientist course in Bangalore or Data Scientist course in Delhi NCR, you may also check for amazing value combo course Data Science Specialization to learn Data Science using Python & R. Also, with our Advance Big Data Science combo course candidates with IT/ ITES background can earn an industry relevant Big Data Science certification at great value for money.


The best Data Science training in Bangalore and Gurgaon, with flexibility of attending data science course online and through self-paced video based mode as well.


Python Data Science Course duration: 180 hours (At least 78 hours live training + Practice and Self-study, with ~8hrs of weekly self-study).

Who Should do this course?

Candidates from various quantitative backgrounds, like Engineering, Finance, Maths, Statistics, Business Management who are not just looking for any Python course, but want Python training with advanced analytics and machine learning skills to head start their career in the field of Data science.

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Course Duration 180 hours
Classes 26
Tools Python
Learning Mode Live/Video Based
Next Batch19th August, 2018 (Gurgaon)
02nd September, 2018 (Bangalore)
30th September, 2018 (Gurgaon)

Introduction to Data Science with Python

  • What is analytics & Data Science?
  • Common Terms in Analytics
  • Analytics vs. Data warehousing, OLAP, MIS Reporting
  • Relevance in industry and need of the hour
  • Types of problems and business objectives in various industries
  • How leading companies are harnessing the power of analytics?
  • Critical success drivers
  • Overview of analytics tools & their popularity
  • Analytics Methodology & problem solving framework
  • List of steps in Analytics projects
  • Identify the most appropriate solution design for the given problem statement
  • Project plan for Analytics project & key milestones based on effort estimates
  • Build Resource plan for analytics project
  • Why Python for data science?

Python: Essentials (Core)

  • Overview of Python- Starting with Python
  • Introduction to installation of Python
  • Introduction to Python Editors & IDE's(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)
  • Understand Jupyter notebook & Customize Settings
  • Concept of Packages/Libraries - Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
  • Installing & loading Packages & Name Spaces
  • Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
  • List and Dictionary Comprehensions
  • Variable & Value Labels –  Date & Time Values
  • Basic Operations - Mathematical - string - date
  • Reading and writing data
  • Simple plotting
  • Control flow & conditional statements
  • Debugging & Code profiling
  • How to create class and modules and how to call them?

Scientific distributions used in python for Data Science

  • Numpy, scify, pandas, scikitlearn, statmodels, nltk etc

Accessing/Importing and Exporting Data using python modules

  • Importing Data from various sources (Csv, txt, excel, access etc)
  • Database Input (Connecting to database)
  • Viewing Data objects - subsetting, methods
  • Exporting Data to various formats
  • Important python modules: Pandas, beautifulsoup

Data Manipulation – cleansing – Munging using Python modules

  • Cleansing Data with Python
  • Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
  • Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
  • Python Built-in Functions (Text, numeric, date, utility functions)
  • Python User Defined Functions
  • Stripping out extraneous information
  • Normalizing data
  • Formatting data
  • Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)

Data Analysis – Visualization using Python

  • Introduction exploratory data analysis
  • Descriptive statistics, Frequency Tables and summarization
  • Univariate Analysis (Distribution of data & Graphical Analysis)
  • Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
  • Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
  • Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc)

Introduction to Statistics

  • Basic Statistics - Measures of Central Tendencies and Variance
  • Building blocks - Probability Distributions - Normal distribution - Central Limit Theorem
  • Inferential Statistics -Sampling - Concept of Hypothesis Testing
  • Statistical Methods - Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square
  • Important modules for statistical methods: Numpy, Scipy, Pandas

Introduction to Predictive Modeling

  • Concept of model in analytics and how it is used?
  • Common terminology used in analytics & modeling process
  • Popular modeling algorithms
  • Types of Business problems - Mapping of Techniques
  • Different Phases of Predictive Modeling

Data Exploration for modeling

  • Need for structured exploratory data
  • EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
  • Identify missing data
  • Identify outliers data
  • Visualize the data trends and patterns

Data Preparation

  • Need of Data preparation
  • Consolidation/Aggregation - Outlier treatment - Flat Liners - Missing values- Dummy creation - Variable Reduction
  • Variable Reduction Techniques - Factor & PCA Analysis

Segmentation: Solving segmentation problems

  • Introduction to Segmentation
  • Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
  • Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
  • Behavioral Segmentation Techniques (K-Means Cluster Analysis)
  • Cluster evaluation and profiling - Identify cluster characteristics
  • Interpretation of results - Implementation on new data

Linear Regression: Solving regression problems

  • Introduction - Applications
  • Assumptions of Linear Regression
  • Building Linear Regression Model
  • Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
  • Assess the overall effectiveness of the model
  • Validation of Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
  • Interpretation of Results - Business Validation - Implementation on new data

Logistic Regression: Solving classification problems

  • Introduction - Applications
  • Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
  • Building Logistic Regression Model (Binary Logistic Model)
  • Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
  • Validation of Logistic Regression Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)
  • Interpretation of Results - Business Validation - Implementation on new data

Time Series Forecasting: Solving forecasting problems

  • Introduction - Applications
  • Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
  • Classification of Techniques(Pattern based - Pattern less)
  • Basic Techniques - Averages, Smoothening, etc
  • Advanced Techniques - AR Models, ARIMA, etc
  • Understanding Forecasting Accuracy - MAPE, MAD, MSE, etc

Machine Learning -Predictive Modeling – Basics

  • Introduction to Machine Learning & Predictive Modeling
  • Types of Business problems - Mapping of Techniques - Regression vs. classification vs. segmentation vs. Forecasting
  • Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
  • Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
  • Overfitting (Bias-Variance Trade off) & Performance Metrics
  • Feature engineering & dimension reduction
  • Concept of optimization & cost function
  • Overview of gradient descent algorithm
  • Overview of Cross validation(Bootstrapping, K-Fold validation etc)
  • Model performance metrics (R-square, Adjusted R-squre, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )

Unsupervised Learning: Segmentation

  • What is segmentation & Role of ML in Segmentation?
  • Concept of Distance and related math background
  • K-Means Clustering
  • Expectation Maximization
  • Hierarchical Clustering
  • Spectral Clustering (DBSCAN)
  • Principle component Analysis (PCA)

Supervised Learning: Decision Trees

  • Decision Trees - Introduction - Applications
  • Types of Decision Tree Algorithms
  • Construction of Decision Trees through Simplified Examples; Choosing the "Best" attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees
  • Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
  • Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules
  • Decision Trees - Validation
  • Overfitting - Best Practices to avoid

Supervised Learning: Ensemble Learning

  • Concept of Ensembling
  • Manual Ensembling Vs. Automated Ensembling
  • Methods of Ensembling (Stacking, Mixture of Experts)
  • Bagging (Logic, Practical Applications)
  • Random forest (Logic, Practical Applications)
  • Boosting (Logic, Practical Applications)
  • Ada Boost
  • Gradient Boosting Machines (GBM)
  • XGBoost

Supervised Learning: Artificial Neural Networks (ANN)

  • Motivation for Neural Networks and Its Applications
  • Perceptron and Single Layer Neural Network, and Hand Calculations
  • Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques
  • Neural Networks for Regression
  • Neural Networks for Classification
  • Interpretation of Outputs and Fine tune the models with hyper parameters
  • Validating ANN models

Supervised Learning: Support Vector Machines

  • Motivation for Support Vector Machine & Applications
  • Support Vector Regression
  • Support vector classifier (Linear & Non-Linear)
  • Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
  • Interpretation of Outputs and Fine tune the models with hyper parameters
  • Validating SVM models

Supervised Learning: KNN

  • What is KNN & Applications?
  • KNN for missing treatment
  • KNN For solving regression problems
  • KNN for solving classification problems
  • Validating KNN model
  • Model fine tuning with hyper parameters

Supervised Learning: Naïve Bayes

  • Concept of Conditional Probability
  • Bayes Theorem and Its Applications
  • Naïve Bayes for classification
  • Applications of Naïve Bayes in Classifications

Text Mining & Analytics

  • Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Creating Term-Document (TxD);Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)
  • Finding patterns in text: text mining, text as a graph
  • Natural Language processing (NLP)
  • Text Analytics – Sentiment Analysis using Python
  • Text Analytics – Word cloud analysis using Python
  • Text Analytics - Segmentation using K-Means/Hierarchical Clustering
  • Text Analytics - Classification (Spam/Not spam)
  • Applications of Social Media Analytics
  • Metrics(Measures Actions) in social media analytics
  • Examples & Actionable Insights using Social Media Analytics
  • Important python modules for Machine Learning (SciKit Learn, stats models, scipy, nltk etc)
  • Fine tuning the models using Hyper parameters, grid search, piping etc.

Project - Consolidate Learnings :

Applying different algorithms to solve the business problems and bench mark the results

Core Python and Pandas Exercises

Solving different exercises related data importing, data manipulation, data processing, data visualization, data exploratory analysis (Univariate, Bi-Variate analysis) etc using different packages like Pandas, Numpy, matplotlib, seaborn

Pandas case study

1. Solving different problems related customer analytics using pandas package 2. Understand the customers spend & repayment behavior and Evaluate areas of bankruptcy, delinquency, and collections etc using pandas packages

Visualization case study

Perform different graphical analysis (bar chart, pie chart, box plot, histogram, stacked charts, heat maps, scatter plots, panel charts etc) for solving different business problems

Credit Card Customers Segmentation

A credit card company wishes to understand its customer behavior so to have an enriched customer profile by having intelligent KPI’s. The idea is to apply advanced algorithms like factor and cluster analysis for data reduction and customer segmentation based on the behavioral data.

Proactive Attrition Management

A wireless telecom companies wants to reduce customer churn by developing a proactive churn management model. The idea is to build a logistic regression based predictive model to develop an incentive plan for enticing would-be churners to remain with the company.

Predicting Loan Default

A bank would like to build credit risk model (application score card using PD models) to accept/ reject applications for loans. Also it wants to understand the key drivers for default or delinquency.

Key Drivers for Customer credit card spending

The objective of this case study is to understand what's driving the total spend of credit card(Primary Card + Secondary card) and identify the key spend drivers . This will require candidates to apply OLS/ linear regression and follow end-to-end model building process and help set the credit limit and designing new product offerings.

Time Series Forecasting

Use time series analysis to forecast the outbound passenger movement for next few quarters.

Sentiment Analysis

Objective of this analysis is to obtain data from Twitter and check how the sentiment varies by country for a particular brand/keyword/company.

Social Media Analytics Case Study

Objective of this analysis is to obtain the data from social media platforms like Twitter/Facebook/Youtube etc and perform different analysis using text mining and Machine learning techniques

Access to 45 hours instructor led live classes of 15x3 hours each, spread over 3 weekends

Video recordings of the class sessions for self study purpose

Weekly assignment, reference codes and study material in PDF format

Module wise case studies/ projects

Career guidance and career support post the completion of some selected assignments and case studies

What if I miss a class?

Don’t worry. You will always get a recording for the class in your Learning Management System (LMS) account. Have a look at that and reach out to the faculty in case of doubts. All our live classes are recorded for self-study purpose and future reference, and these can also be accessed through our LMS. Hence, in case you miss a class, you can refer to the video recording and then reach out to the faculty during their doubts clearing time or ask your question in the beginning of the subsequent class.

You can also repeat any class you want in the next one year after your course completion. Batch change policies will however, apply in this case.

Please note that incase you are not able to complete your course within one year of course validity, due to reasons at your end, limited support might be available post the completion of one year.

For how long are the recordings available to me?

1 year post your course completion. If needed, you can also repeat any number of classes you want in the next one year after course completion. Batch change policies will however, apply in this case.

In case required because any genuine reasons, the recordings access can be extended further for upto 1 year post the completion of one year validity. Please note that given the constant changes in the Analytics industry, our courses continue to be upgraded and hence old courses might no longer hold relevance. Hence, we do not promise lifetime access just for marketing purposes. 

Can I download the recordings?

No. Our recordings can be accessed through your account on LMS or stream them live online at any point of time though.

Recordings are integral part of AnalytixLabs intellectual property by Suo Jure. The downloading/distribution of these recordings in anyway is strictly prohibited and illegal as they are protected under copyright act. Incase a student is found doing the same, it will lead to an immediate and permanent suspension in the services, access to all the learning resources will be blocked, course fee will be forfeited and the institute will have all the rights to take strict legal action against the individual.

What if I share my LMS login details with a friend?

The sharing of LMS login credentials is unauthorized, and as a security measure, if the LMS is accessed by multiple places, it will flag in the system and your access to LMS can be terminated.

Will I get a certificate in the end?

Yes. All our course are certified. As part of the course, students get weekly assignments and module-wise case studies. Once all your submissions are received and evaluated well, the certificate shall be awarded.

Please note that incase you are not able to complete the course within the one year validity, AnalytixLabs might hold a mock interview/viva, apart from your submissions, before issuing the certificate.

Do you help in placements?

We follow a comprehensive and a self-sustaining system to help our students with placements. This is a win-win situation for our candidates and corporate clients. As a pre-requisite for learning validation, candidates are required to submit the case studies and project work provided as a part of the course (flexible deadline). Support from our side is continuous and encompasses help in profile building, CV referrals (as and when applicable) through our ex-students, HR consultants and companies directly reaching out to us.

We will provide guidance to you in terms of what are the right profiles for you based on your education and experience, interview preparation and conducting mock interviews, if required. The placement process for us doesn’t end at a definite time post your course completion, but is a long relationship that we will like to build.

To avoid any kind of ambiguity, we strongly suggest that you go through our course brochures.

Please also note that incase you are unable to complete the course in the one year course validity period, we might not be able to assist with placements. The discussion you would have had with our counsellors/faculty at the time of registration is also basis the profile you hold at that time. Incase your course has got delayed due to reasons at your end, this might effect the profile, in which case it might be difficult for us to assist in placements.

Do you guarantee placements?

No institute can guarantee placements, unless they are doing so as a marketing gimmick! It is on a best effort basis.

In professional environment, it is not feasible for any institute to do so, except for a marketing gimmick. For us, it is on a best effort basis but not time – bound – in some cases students reach out to us even after 3 years for career support.

Do you have a classroom option?

Yes we have classroom option for Delhi-NCR candidates. However, most of our students end up doing instructor led live online classes, including those who join classroom in the beginning. Based on the student feedback, the learning experience is same both in classroom and instructor led live online fully interactive mode.

How do I attend the online classes? Are they interactive or self-paced?

We provide both the options and for instructor led live online classes we use the gold standard platform used by the top universities across the globe. These video sessions are fully interactive and students can chat or even ask their questions verbally over the VoIP in real time to get their doubts cleared.

What do I need to attend the online classes?

To attend the online classes, all you need is a laptop/PC with a basic internet connection. Students have often shared good feedback of attending these live classes through their data card or even their mobile 3G connection, though we recommend a basic broadband connection.

For best user experience, a mic-headphone is recommended to enhance the voice quality, though the laptop’s in-built mic works fine and you can ask your question over the chat as well.

How can I reach out to someone if I have doubts post class?

Through the LMS, students can always connect with the trainer or even schedule one-to-one time over the phone or online. During the course we also schedule periodic doubts-clearing classes though students can also ask doubts of a class in the subsequent class.

LMS also has a discussion forum where a lot of your doubts might get easily answered.

Incase you are having a problem still, repeat the class and schedule one-to-one time with the trainer.

What is your refund policy?

  • Instructor Led Live online or Classroom - Within 7 days of registartion date and latest 3 days before batch start
  • Video-based - 2 days

Can I pay in installments?

Not for this course. The instalment options are available only for our courses which are atleast 3 months long.

I am having difficulty coping up with my classes. What can I do?

For all the courses, we also provide the recordings of each class for their self-reference as well as revision in case you miss any concept in the class. In case you still have doubts after revising through the recordings, you can also take one-to-one time with the faculty outside classes during. Furthermore, if students want to break their courses in different modules, they get one year time to repeat any of the classes with other batches.

What are the system requirements for the software?

There is no particular system requirement for this course since the tool required for this course (Python) can easily be installed on almost every laptop with basic configuration available these days.

AnalytixLabs is one of the best institutes for learning advanced analytics. I found their SAS material best, from coverage point of view and the pace at which they teach. As for the tutors Sumeet, Chandra and Ankita are some of the best names in this industry and their experience proves it. They take special efforts to make sure your doubts are cleared. Till now I have not seen any professional training institute that grooms you holistically like AnalytixLabs does.

- Dhananjay Sharma (Food Panda)

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