Data Science Course in Pune

Data Science is used to apply critical analysis, provide the ability to develop sophisticated models, for massive data sets and drive business insights. Data Science utilizes the potential and scope of machine learning implementation, by making use of Mahout. Data Science Course in Pune with Data Interpretation for Business Intelligence
  • Learn from Industry Experts
  • Most number of Instructor led sessions
  • Practical Training on Real Time Projects
  • Assured Placement Support
  • 100% Positive Feedbacks

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No. of Sessions



Deep Learning

Basics Only

In Detail(14-16 Sessions)

Class Mode

Online Classes

Online & Classroom

Fees Installment

Installments NOT Allowed

Convenient Installments


No Placement Support

100% Placement Support

  1. Defining Python
  2. History of Python and its Growing Popularity
    Features of Python and its Wide Functionality
  3. Python 2 vs Python 3
  4. Setting Up Python
  5. Environment for Development
  6. What and How of Python Installation?
  7. IDEs: IDLE, Pycharm, and Jupyter
  8. Writing First Python Program
  9. Python Scripts on UNIX and Windows
  10. Installation on Ubuntu-based Machines
  11. Programming on Interactive Shell
  12. Python Identifiers and Keywords
  13. Indentation in Python
  14. Comments and Writing to the Screen
  15. Command Line Arguments and Flow Control
  16. User Input
  17. Python Core Objects
  18. Defining Built-in Functions
  19. Objectives
  20. Variables and their types
  21. Variables – String Variables
  22. Variables – Numeric Types
  23. Variables – Boolean Variables
  24. Boolean Object and None Object
  25. Tuple Object and Operations
  26. Dictionary Object and Operations
  27. Types of Variables – Dictionary
  28. Comparison of Variables
  29. Dictionary Methods and Manipulations
  30. Operators and Logical Operators
  31. Data Structures and Data Processing
  32. Arithmetic Operations on Numeric Values
  33. Operators and Keywords for Sequences
  34. Understanding Conditional Statements
  35. Break Statements and Continue Statements
  36. Using Indentations for defining if & else block
  37. Loops in Python
  38. While, Nested, Demo-Create
  39. How to Control Loops?
  40. Sequence and Iterable Objects
  1. Objectives of Functions
  2. Types of Functions
  3. Creating UDF Functions
  4. Function Parameters
  5. Unnamed and Named Parameters
  6. Creating and Calling Functions
  7. Python user Defined Functions
  8. Python packages Functions
  9. Anonymous Lambda Function
  10. Understanding String Object Functions
  11. List and Tuple Object Functions
  12. Studying Dictionary Object Functions
  13. Defining Python Inbuilt Modules
  14. Studying Types of Modules
  15. os, sys, time, random, datetime, zip modules
  16. How to Create Python User Defined Modules?
  17. Understanding Pythonpath
  18. Creating Python Packages
  19. init File and Package Initialization
  20. What and How of File Handling with Python?
  21. How to Process Text Files using Python?
  22. Read/Write and Append File Object
  23. Test Operations: os.path
  24. Overview of Object Oriented Programming
  25. Defining Classes, Objects, and Initializers
  26. Attributes – Built-In Class
  27. Destroying Objects
  28. Methods – Instance, Class, Static, Private methods, and Inheritance
  29. Data Hiding
  30. Module Aliases and reloading modules
  31. Python Exceptions Handling
  32. Standard Exception Hierarchy
  33. .. except…else
  34. .. finally…clause
  35. Creating Self-Exception Class
  36. User-defined Exceptions
  37. Debugging Errors – Unit Tests
  38. Project Skeleton
  39. Creating and Using the Skeleton
  40. How to use pdb debugger?
  41. Using Pycharm Debugger
  42. Asserting Statement for Debugging
  43. Using UnitTest Framework for Testing
  44. Understanding Regular Expressions
  45. Match Function, Search Function, and the Comparision
  46. Compile and Match, Match and Search
  47. Search and Replace
  48. What and How of Extended Regular Expressions?
  49. Wildcard Characters
  1. Data Visualization and Matplotlib, seaborn
  2. Python Libraries
  3. Features of Matplotlib
  4. Line Properties Plot with (x, y)
  5. Set Axis, Labels, and Legend Properties
  6. Alpha and Annotation
  7. Univariate plots
  8. Bivariate plots
  9. Multivariate plots
  10. Interpretations

• Data Manipulation and Machine Learning with Python
• Data Manipulation with Python – Pandas
• Understanding Pandas
• Defining Data Structures
• Data Operations(filtering, sorting, grouping, aggregation, merging) and Data Standardization
• Pandas: File Read and Write Support
• SQL Operations(pandasql)

• Exploring and Understanding Data
• Exploring Numeric Variables
• Understanding Types of Data
• Qualitative and Quantitative Analysis
• Studying Descriptive Statistics
• Exploring Numeric Variables
• Measuring the Central Tendency – The Model
• Measuring Spread – Variance and Standard Deviation
• Visualising Numeric Variables – Boxplots and Histograms
• Understanding Numeric Data – Uniform and Normal Distributions
• Measuring the Central Tendency – The Mode
• Exploring Relationships between Variables
• Visualizing Relationships – Scatterplots
• Nominal and Ordinal Measurement
• Interval and Ratio Measurement
• Statistical Investigation
• Inferential Statistics
• Probability and Central Limit Theorem
• Exploratory Data Analysis
• Normal Distribution
• Distance Measures
• Euclidean & Manhattan Distance
• Minkowski & Mahalanobis
• Cosine
• Correlation
• PPMC (Pearson Product Moment Correlation)

• Importance of Hypothesis Testing in Business
• Null and Alternate Hypothesis
• Understanding Types of Errors
• Contingency Table and Decision Making
• Confidence Coefficient
• Upper Tail Test
• Understanding Parametric Tests
• Z-Test and Z-Test in R
• Chi-Square Test
• Degree of Freedom
• One-Way ANOVA Test
• F-Distribution, F-Ration Test

Regression Methods for Forecasting Numeric Data

Regression Methods for Forecasting Numeric Data
• Understanding Neural Networks
• From Biological to Artificial Neurons
• Activation Functions
Deep Learning – Neural Networks and Support Vector Machines
• What is Regression?
• Model Selection
• Generalized Regression
• Simple Linear Regression
• Multiple Linear Regression
• Correlations
• Correlation between X and Y
• Ridge and Regularized Regression
• Time Series
• Prediction: Time Dependent/Variant Data
• Ordinary Least Square Regression Model
• Dummy Variable Regression Model
• Interaction Regression Model
• Non-Linear Regression Model
• Perform Regression Analysis with Multiple Variables
• Network Topology
• Recurrent and Gaussian Neural Network
• The Number of Layers
• The Direction of Information Travel
• The Number of Nodes in Each Layer
• Training Neural Networks with Backpropagation
• Support Vector Machines
• Classification with Hyperplanes
• Finding the Maximum Margin
• The Case of Linearly Separable Data
• The Case of Non-Linearly Separable Data
• Retrieve Data using SQL Statements
• Using Kernels for Non-Linear Spaces


• K-NN, Naïve Bayes, Support Vector Machines
• Defining Classification
• Understanding Classification and Prediction
• Decision Tree Classifier
• How to Build Decision Trees?
• Basic Algorithm for a Decision Tree
• Decision Trees and Data Mining
• Random Forest Classifier
• Features of Random Forests
• Out of Box Error Estimate and Variable Importance
• Naïve Bayes Classifier Model
• Bayesian Theorem
Advantages and Disadvantages of Naïve Bayes Classifier Model
• Understanding Support Vector Machines
• Understanding Linear SVMs
• Logistic Regression
• Bagging and Boosting(Adaboost)

• Understanding K-means Clustering
• K-means and Pseudo Code
• K-means Clustering using R
• TF-IDF and Cosine Similarity
• Application to Vector Space Model
• What is Hierarchical Clustering?
• Hierarchical Clustering Algorithm
• Understanding Agglomerative Clustering Process
• DBSCAN Clustering
• What is Association Rule Mining?
• Association Rule Strength Measures
• Checking Apriori Algorithms
• Ordering Items
• Understanding Candidate Generation
• Performing Visualisation on Associated Rules
• Dimensionality reduction

Frequently asked questions

Two types: Descriptive(focuses on past data to understand past/present trends) and Predictive(focuses on past data to predict unknown/future)..

Data analyst generally works on creation of reports based on company’s data driven KPI’s(generally involves descriptive analytics), whereas Data scientists understand business and domain along with the technicalities to understand what will happen in future(more on descriptive + predictive analytics both)

Machine Learning algorithms are mathematical in nature, hence you need to first understand that part(includes statistics, probability theory, Linear Algebra).

Once you know this part, then in order to implement these algorithms on a real life data set, you need a language which contains modules which simplify ML development(like MATLAB, Python, R etc)

So, to sum it up:

-Data manipulation/preprocessing
-Machine Learning Algos
-Lots of scenarios
-End to End projects
-Deployment of Models on various cloud platforms.

Every person who has done some online course or has went through some tutorial uploads a CV for a JOB. But not necessary everyone gets it.

In order for you to get jobs, your skill level has to be detailed, your knowledge cannot be limited to generics. If you feel that by doing some sort of a crash course will get you anywhere, then I wish you luck.

By the time you have completed the course you should be able to handle complex scenarios with efficiency and measured approach(without any help of course).

So a simple advice: Join a detailed course.

The answer to this is relative based on your previous experience and technology. Kindly have a word with our technical counsellor regarding the same.

The term Data Science is used interchangeably with Datalogy.
Data Science employs its theories and techniques from physics, mathematics, nanotechnologies and this list goes to 23 fields.
Data Science is considered to be a part of many academic and research areas.
Data Science has been employed in fraud monitoring and security.
Data Analytics is now increasingly used in multiple sectors and these sectors owe their success to Data Science and Data Analytics

Various companies have certifications available for these kind of programs:

AWS Certified Machine Learning – Specialty certification
Professional Data Engineer Certification(Google)
Google Data Analytics Professional Certificate
Microsoft Certified: Azure Data Scientist Associate
Data science professionals(IBM)
and the list goes on.

Graduation in any stream, Freshers or working professionals who either wish to start their career as a Data Scientist or wish to switch from their previous profile into mainstream analytics.

Training students for leading brands