

Fast Track your career in Data Science AI & ML with SkillEnable!
Master all 3 Data Science elements – Statistics, Tools & Business Knowledge – with this complete hands-on & comprehensive program
Deep Dive in Data Science AI & ML
Join Booming Data
Analytics Industry
SkillEnable focuses on developing you as the prime target of recruiters by developing interpersonal and interview skills along with regular curriculum.
Become a Target Hire
SkillEnable focuses on developing you as the prime target of recruiters by developing interpersonal and interview skills along with a regular curriculum.
Industry Relevant Tools
Get hands-on learning experience of various relevant industry tools and also practice real-life cases studies and Capstone projects using the Latest Data Science tools.
Tailor-made Curriculum
Individual candidate focused curriculum and soft skill enhancement.




Who is it for?
Recommended For:
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Data Science Enthusiasts
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Graduates
Eligibility Criteria:
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Individuals with basics knowledge in Programming & Statistics
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Should be ready for an extremely intensive and demanding program.
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Should be a motivated individual who is looking to get hired in the data science industry or switch their career in the field of data science.
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What is Data?
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What is Data Science?
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What does Data Sc. Involve?
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Tools of data Sc.
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What is Machine Learning?
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Where is Machine Learning used?
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Job Roles
Unit 1
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Introduction to Tableau & Installation
- Versions of Tableau & its' Utility
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Importing Data & User Interface
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Basics of Excel
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Dimension & Measure
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Introduction to Tableau Commands
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Discrete Vs. Continuous Data
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Aggregations in Tableau
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Creating Charts
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Types of Charts
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Bar Charts
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Stacked Bar Charts
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Line Charts
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Scatter Plot
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Area Charts
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Pie Charts
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Tree Maps
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Heat Maps
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Bubble Charts
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Bullet Charts
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Box & Whisker plots
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Pareto Charts
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Histograms
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Gantt Charts
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Donut Charts
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Funnel Charts
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Waterfall Charts
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Tableau Storyline
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Set, Parameters & Groups
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Tableau IF Statements
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Case Statements of Tableau
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Tableau Functions
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String Function
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Table Calculations
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Rank Functions
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Aggregate Functions
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Date Functions
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Window Sum
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LOD Expressions
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Look Up Functions
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Fixed Functions
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Count Distinct
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Windows Functions
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Sortings
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Filters
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Types of Filter
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Dimension Filter
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Measure Filter
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Visual Filter
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Context Filter
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Create Dashboard
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Design Dashboard
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Adding Filters in Tableau Dashboard
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Tableau Workbook
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Reporting Tools
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Story with Dashboard
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Interactive Filter
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Data Source Filter
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Parameter Filter
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Edit Data Source
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Unions
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Joins
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Data Blending
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Creating Set in Tableau
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Pivot in Tableau
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Forecast in Tableau
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Map Layers
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Tableau Group By
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Hierarchy
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Tableau User Group
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Introduction to Tableau Prep
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Data Connections
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Visual Analytics and Case Study Problem statement discussion
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Case Study and assessment solution discussion
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Dashboard & Stories, Advanced Charts and Case Study Problem statement discussion
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Case Study Presentation, Revision, Interview Preparation
Unit 2 Week 01-03
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Introduction to SQL Server
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Oracle Vs. Microsoft SQL Server
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Installing and Configuring SQL Server
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MS Server Management Studio Instatllation
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Working with Databases and Storage
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Planning and Implementing a Backup Strategy
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Restoring SQL Server 2014 Databases
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Importing and Exporting Data
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Monitoring SQL Server 2014
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Tracing SQL Server Activity
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Managing SQL Server Security
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Performing Ongoing Database Maintenance
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Industry based Projects
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SQL Server Developments (T-SQL) *Topics provided below
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1. T-SQL - OVERVIEW
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2. T-SQL SERVER - DATA TYPES
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3. T-SQL SERVER - CREATE TABLES
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4. T-SQL SERVER - DROP TABLES
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5. T-SQL SERVER - INSERT STATEMENT
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6. T-SQL SERVER - SELECT STATEMENT
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7. T-SQL SERVER - UPDATE STATEMENT
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8. T-SQL SERVER - DELETE STATEMENT
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9. T-SQL SERVER - WHERE CLAUSE
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10. T-SQL SERVER - LIKE CLAUSE
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11. T-SQL SERVER - ORDER BY CLAUSE
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12. T-SQL SERVER - GROUP BY CLAUSE
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13. T-SQL SERVER - DISTINCT CLAUSE
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14. T-SQL SERVER - JOINING TABLES
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15. T-SQL SERVER - SUB-QUERIES
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16. T-SQL SERVER - STORED PROCEDURES
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17. T-SQL SERVER – TRANSACTIONS
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18. T-SQL SERVER - INDEXES
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19. T-SQL SERVER - SQL FUNCTIONS
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20. T-SQL SERVER - STRING FUNCTIONS
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21. T-SQL SERVER - DATE FUNCTIONS
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22. T-SQL SERVER - NUMERIC FUNCTIONS
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23. T-SQL SERVER – Create Stored Procedure
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24. T-SQL SERVER – Alter Stored Procedure
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25. T-SQL SERVER – Delete Stored Procedure
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26. T-SQL SERVER – Create View
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27. T-SQL SERVER – Alter View
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28. T-SQL SERVER – Delete View
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Normalizations
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ER Diagrams in SQL Server
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Industry based Projects
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Introduction to Python
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Computer Programming Data Types
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Variables
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Basic Input-Output Operations
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Basic Operators
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Boolean Values
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Conditional Execution
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Loops
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Lists
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Logical and Bitwise Operations
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Functions
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Tuples
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Dictionaries
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Data Processing
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Modules
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Packages
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String and List Methods
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Exceptions
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The Object-Oriented Approach: Classes
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Methods
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Objects and the Standard Objective Features
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Exception Handling
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Working with Files
Unit 3 Week 04-07
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Introduction to probability
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Sampling and sampling distributions
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Unions of Events and Addition Rules
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Unions of Events and Addition Rules
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Conditional Probability
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Independence
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Random Variables
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Baye'S Theorem
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Average, Mean, Median, Mode
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Range, Quartiles & Percentiles
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Interquartiles Range
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R-SQUARED
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Variance & Standard Deviation
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Statistical Inference
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Normal Distribution
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Standard Normal Distribution
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Poisson Distribution
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Bernoulli Distribution
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Point Estimate
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Confidence level & Confidence Intervals
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Margin of Error
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Population Means
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Hypothesis Testing
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Hypothesis Testing : Two Sample Test
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Hypothesis Testing Proportion & Mean
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Z- Test & T-Test
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One-tailed and two-tailed tests
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The F distribution
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The chi-square distribution
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The chi-square test of independence
Unit - 4 Week 08-10
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Introduction to Python
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Computer Programming Data Types
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Variables & Data Types
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Basic Input-Output Operations
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Basic Operators
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Boolean Values
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Strings
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Lists & Tuples
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Dictionary
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Sets
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Conditional Expressions
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Loops
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Logical and Bitwise Operations
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Functions & Recursions
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File Input & Output
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Exceptions handling
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Object Oriented Programming
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Virtual environment & Python libraries
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Some special functions in python e.g Lambda, bin, format, map, filter, reduce
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Web scraping
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Ipython
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Jupyter
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NumPy Array
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Sorting Array
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NumPy Universal Functions
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Array Indexing: Accessing Single Elements
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Array Slicing: Accessing Subarrays
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Reshaping of Arrays
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Array Concatenation and Splitting
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NumPy Arrays: Universal Functions
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Aggregations: Min, Max, and Everything in Between
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Sorting Arrays
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Fast Sorting in NumPy: np.sort and np.argsort
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Partial Sorts: Partitioning
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Structured Data: NumPy’s Structured Arrays
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Pandas Series Object
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Pandas DataFrame Object
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Pandas Index Object
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Data Indexing and Selection
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Operating on Data in Pandas
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Handling Missing Data
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Hierarchical Indexing
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Combining Datasets: Concat and Append
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Combining Datasets: Merge and Join
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Aggregation and Grouping
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Pivot Tables
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Vectorized String Operations
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Working with Time Series
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Motivating query() and eval(): Compound Expressions
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pandas.eval() for Efficient Operations
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DataFrame.eval() for Column-Wise Operations
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DataFrame.query() Method
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Visualization with Matplotlib
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Line Plots
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Scatter Plots
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Scatter Plots with plt.scatter
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Visualizing Errors
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Density and Contour Plots
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Histograms, Binnings, and Density
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Customizing Plot Legends, Colorbars
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Multiple Subplots
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Text and Annotation
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Configurations and Stylesheets
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Three-Dimensional Plotting in Matplotlib
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Basemap
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Visualization with Seaborn
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Matplotlib Vs. Seaborn
Unit 5 Week 11 - 15
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Introduction
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Types of Machine Learning
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Supervised Vs. Unsupervised Learning
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Instance-Based Vs. Model-Based Learning
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Insufficient quantity of Training Data
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Nonrepresentative Training Data
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Poor-Quality Data
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Irrelevant Features
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Overfitting the Training Data
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Underfitting the Training Data
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Supervised Learning & Types
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Classifiction & Types
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Regression & Types
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Acuracy Metrics
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Training a Binary Classifier
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Performance Measures
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Measuring Accuracy Using Cross-Validation
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Confusion Matrix
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Precision and Recall
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Multiclass Classification
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Error Analysis
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Multilabel Classification
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Multioutput Classification
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Projection & Manifold Learning
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Principal Component Analysis
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Data wrangling
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Gradient Descent
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Polynomial Regression
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Learning Curves
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Ridge, Lasso Regression
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Elastic Net & Early Stopping
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Logistic Regression
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Training and Cost Function
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Sigmoid Probability
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Accuracy Matrix
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Decision Boundaries
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Linear SVM & Classification
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SVM : Linear Separability
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SVM : Mathematical Representation
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Nonlinear SVM & Classification
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SVM Regression
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Kernal Trick
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Decision Function Classifier
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Overfitting
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Random Forest Classifier
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Performance Measure Confusion Matrix & Cost Matrix
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Estimating Class Probabilities
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The CART Training Algorithm
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Gini Impurity or Entropy?
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Regularization Hyperparameters
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Instability
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Overview & Applications
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Types of Unsupervised Learning
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Overview
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Hierarchical Clustering
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Ensemble Learning
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Bagging and Pasting with Scikit-Learn
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Out-of-Bag Evaluation
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Random Patches and Random Subspaces
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Random Forests
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AdaBoost
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Gradient Boosting
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XGBoost & Parameters
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Model Selection
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Overview of Time Series Modeling
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Time Series Patterns & Types
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White Noise
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Stationarity
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Removal of Non-Stationarity
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Time Series Models
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Purpose of Recommender Systems
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Paradigms of Recommender Systems
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Collaborative Filtering
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Association Rule Mining
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Text Mining: Overview & Significance
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Application
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Text Extraction & Preprocessing
Unit 6 Week 16-20
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Introduction
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Why Tensorflow
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Installation
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Node Value
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Linear Regression with Tensorflow
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Gradient Descent
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Training Machine Learning Program with Tensorflow API
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Training Deep Neural Nets
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Distributing Tensorflow across servers
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Convolutional Neural Network
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Recurrent Neural Network
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Transfer Learning
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Autoincoders
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Reinforcement Learning
Unit Week - 21-25
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Quantitative Reasoning
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Logical Reasoning
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Critical Reasoning
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Integrated Reasoning
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Verbal Reasoning
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Integrated Reasoning
40 Hours Of Aptitude
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How to build Resume
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How to write Cover Letter
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LinkedIn Profile Building Session
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Git Hub Profile Making
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How to do Job Application
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How to give PPT Presentation
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Telephonic Etiquette
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Email Etiquette
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Mock Interviews
20 Hours of Soft Skills Training

Training from highly experienced instructors.

100% live online learning.

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240 hours of live exhaustive training sessions split over 24 weeks.

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40 hours of exhaustive holistic development training and 20 hours of SoftSkill Training

Learn over 10 industry-relevant tools.

3 month compulsory internship for freshers as a part of the curriculum.
Course Details





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Apply your skills with hands-on projects & Case Studies.
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No prior coding experience is required to do this course.
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Industry Approved Certificate upon completion.

Unbeaten Salary
Data is the fuel of the 21st Century. The demand for data scientists is high, making it a lucrative career option.

Rising Demand
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Top Skill
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Increasing use of Data Science in areas of Healthcare, Education, Agribusiness industries creates a huge demand for Data Scientists.

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