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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.

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Who is it for?

Recommended For:

  • Data Science Enthusiasts

  • Graduates

Eligibility Criteria:

  • Individuals with basics knowledge in Programming & Statistics

  • Should be ready for an extremely intensive and demanding program.

  • 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.

  • What is Data?

  • What is Data Science?

  • What does Data Sc. Involve?

  • Tools of data Sc.

  • What is Machine Learning?

  • Where is Machine Learning used?

  • Job Roles

Unit 1 

  • Introduction to Tableau & Installation

  • Versions of Tableau & its' Utility
  • Importing Data & User Interface

  • Basics of Excel

  • Dimension & Measure

  • Introduction to Tableau Commands

  • Discrete Vs. Continuous Data

  • Aggregations in Tableau

  • Creating Charts

  • Types of Charts

  • Bar Charts

  • Stacked Bar Charts

  • Line Charts

  • Scatter Plot

  • Area Charts

  • Pie Charts

  • Tree Maps

  • Heat Maps

  • Bubble Charts

  • Bullet Charts

  • Box & Whisker plots

  • Pareto Charts

  • Histograms

  • Gantt Charts

  • Donut Charts

  • Funnel Charts

  • Waterfall Charts

  • Tableau Storyline

  • Set, Parameters & Groups

  • Tableau IF Statements

  • Case Statements of Tableau

  • Tableau Functions

  • String Function

  • Table Calculations

  • Rank Functions

  • Aggregate Functions

  • Date Functions

  • Window Sum

  • LOD Expressions

  • Look Up Functions

  • Fixed Functions

  • Count Distinct

  • Windows Functions

  • Sortings

  • Filters

  • Types of Filter

  • Dimension Filter

  • Measure Filter

  • Visual Filter

  • Context Filter

  • Create Dashboard

  • Design Dashboard

  • Adding Filters in Tableau Dashboard

  • Tableau Workbook

  • Reporting Tools

  • Story with Dashboard

  • Interactive Filter

  • Data Source Filter

  • Parameter Filter

  • Edit Data Source

  • Unions

  • Joins

  • Data Blending

  • Creating Set in Tableau

  • Pivot in Tableau

  • Forecast in Tableau

  • Map Layers

  • Tableau Group By

  • Hierarchy

  • Tableau User Group

  • Introduction to Tableau Prep

  • Data Connections

  • Visual Analytics and Case Study Problem statement discussion

  • Case Study and assessment solution discussion

  • Dashboard & Stories, Advanced Charts and Case Study Problem statement discussion

  • Case Study Presentation, Revision, Interview Preparation

Unit 2  Week 01-03 

  • Introduction to SQL Server

  • Oracle Vs. Microsoft SQL Server

  • Installing and Configuring SQL Server

  • MS Server Management Studio Instatllation

  • Working with Databases and Storage

  • Planning and Implementing a Backup Strategy

  • Restoring SQL Server 2014 Databases

  • Importing and Exporting Data

  • Monitoring SQL Server 2014

  • Tracing SQL Server Activity

  • Managing SQL Server Security

  • Performing Ongoing Database Maintenance

  • Industry based Projects

  • SQL Server Developments (T-SQL) *Topics provided below

  • 1. T-SQL - OVERVIEW

  • 2. T-SQL SERVER - DATA TYPES

  • 3. T-SQL SERVER - CREATE TABLES

  • 4. T-SQL SERVER - DROP TABLES

  • 5. T-SQL SERVER - INSERT STATEMENT

  • 6. T-SQL SERVER - SELECT STATEMENT

  • 7. T-SQL SERVER - UPDATE STATEMENT

  • 8. T-SQL SERVER - DELETE STATEMENT

  • 9. T-SQL SERVER - WHERE CLAUSE

  • 10. T-SQL SERVER - LIKE CLAUSE

  • 11. T-SQL SERVER - ORDER BY CLAUSE

  • 12. T-SQL SERVER - GROUP BY CLAUSE

  • 13. T-SQL SERVER - DISTINCT CLAUSE

  • 14. T-SQL SERVER - JOINING TABLES

  • 15. T-SQL SERVER - SUB-QUERIES

  • 16. T-SQL SERVER - STORED PROCEDURES

  • 17. T-SQL SERVER – TRANSACTIONS

  • 18. T-SQL SERVER - INDEXES

  • 19. T-SQL SERVER - SQL FUNCTIONS

  • 20. T-SQL SERVER - STRING FUNCTIONS

  • 21. T-SQL SERVER - DATE FUNCTIONS

  • 22. T-SQL SERVER - NUMERIC FUNCTIONS

  • 23. T-SQL SERVER – Create Stored Procedure

  • 24. T-SQL SERVER – Alter Stored Procedure

  • 25. T-SQL SERVER – Delete Stored Procedure

  • 26. T-SQL SERVER – Create View

  • 27. T-SQL SERVER – Alter View

  • 28. T-SQL SERVER – Delete View

  • Normalizations

  • ER Diagrams in SQL Server

  • Industry based Projects

  • Introduction to Python

  • Computer Programming Data Types

  • Variables

  • Basic Input-Output Operations

  • Basic Operators

  • Boolean Values

  • Conditional Execution

  • Loops

  • Lists

  • Logical and Bitwise Operations

  • Functions

  • Tuples

  • Dictionaries

  • Data Processing

  • Modules

  • Packages

  • String and List Methods

  • Exceptions

  • The Object-Oriented Approach: Classes

  • Methods

  • Objects and the Standard Objective Features

  • Exception Handling

  • Working with Files

Unit 3 Week 04-07

  • Introduction to probability 

  • Sampling and sampling distributions

  • Unions of Events and Addition Rules

  • Unions of Events and Addition Rules

  • Conditional Probability

  • Independence

  • Random Variables

  • Baye'S Theorem 

  • Average, Mean, Median, Mode

  • Range, Quartiles & Percentiles

  • Interquartiles Range

  • R-SQUARED

  • Variance  & Standard Deviation

  • Statistical Inference

  • Normal Distribution

  • Standard Normal Distribution

  • Poisson Distribution

  • Bernoulli Distribution

  • Point Estimate

  • Confidence level & Confidence Intervals

  • Margin of Error

  • Population Means

  • Hypothesis Testing

  • Hypothesis Testing : Two Sample Test

  • Hypothesis Testing Proportion & Mean

  • Z- Test & T-Test

  • One-tailed and two-tailed tests

  • The F distribution

  • The chi-square distribution

  • The chi-square test of independence

Unit - 4 Week 08-10

  • Introduction to Python

  • Computer Programming Data Types

  • Variables & Data Types

  • Basic Input-Output Operations

  • Basic Operators

  • Boolean Values

  • Strings

  • Lists & Tuples

  • Dictionary

  • Sets

  • Conditional Expressions

  • Loops

  • Logical and Bitwise Operations

  • Functions & Recursions

  • File Input & Output

  • Exceptions handling

  • Object Oriented Programming

  • Virtual environment & Python libraries

  • Some special functions in python e.g Lambda, bin, format, map, filter, reduce

  • Web scraping

  • Ipython

  • Jupyter

  • NumPy Array

  • Sorting Array

  • NumPy Universal Functions

  • Array Indexing: Accessing Single Elements 

  • Array Slicing: Accessing Subarrays

  • Reshaping of Arrays

  • Array Concatenation and Splitting 

  • NumPy Arrays: Universal Functions 

  • Aggregations: Min, Max, and Everything in Between 

  • Sorting Arrays

  • Fast Sorting in NumPy: np.sort and np.argsort 

  • Partial Sorts: Partitioning 

  • Structured Data: NumPy’s Structured Arrays 

  • Pandas Series Object 

  • Pandas DataFrame Object 

  • Pandas Index Object

  • Data Indexing and Selection

  • Operating on Data in Pandas 

  • Handling Missing Data

  • Hierarchical Indexing

  • Combining Datasets: Concat and Append 

  • Combining Datasets: Merge and Join

  • Aggregation and Grouping 

  • Pivot Tables 

  • Vectorized String Operations

  • Working with Time Series 

  • Motivating query() and eval(): Compound Expressions 

  • pandas.eval() for Efficient Operations 

  • DataFrame.eval() for Column-Wise Operations 

  • DataFrame.query() Method 

  • Visualization with Matplotlib

  •  Line Plots 

  • Scatter Plots

  • Scatter Plots with plt.scatter

  • Visualizing Errors 

  • Density and Contour Plots

  • Histograms, Binnings, and Density

  • Customizing Plot Legends, Colorbars

  • Multiple Subplots 

  • Text and Annotation

  • Configurations and Stylesheets

  • Three-Dimensional Plotting in Matplotlib

  • Basemap

  • Visualization with Seaborn

  • Matplotlib Vs. Seaborn

Unit 5 Week 11 - 15

  • Introduction

  • Types of Machine Learning

  • Supervised Vs. Unsupervised Learning

  • Instance-Based Vs. Model-Based Learning

  • Insufficient quantity of Training Data

  • Nonrepresentative Training Data

  • Poor-Quality Data

  • Irrelevant Features

  • Overfitting the Training Data

  • Underfitting the Training Data

  • Supervised Learning & Types

  • Classifiction & Types

  • Regression & Types

  • Acuracy Metrics

  • Training a Binary Classifier

  • Performance Measures

  • Measuring Accuracy Using Cross-Validation

  • Confusion Matrix

  • Precision and Recall

  • Multiclass Classification

  • Error Analysis

  • Multilabel Classification

  • Multioutput Classification

  • Projection & Manifold Learning

  • Principal Component Analysis

  • Data wrangling

  • Gradient Descent

  • Polynomial Regression

  • Learning Curves

  • Ridge, Lasso Regression

  • Elastic Net & Early Stopping

  • Logistic Regression

  • Training and Cost Function

  • Sigmoid Probability

  • Accuracy Matrix

  • Decision Boundaries

  • Linear SVM & Classification

  • SVM : Linear Separability

  • SVM : Mathematical Representation

  • Nonlinear SVM & Classification

  • SVM Regression

  • Kernal Trick

  • Decision Function Classifier

  • Overfitting

  • Random Forest Classifier

  • Performance Measure Confusion Matrix & Cost Matrix

  • Estimating Class Probabilities

  • The CART Training Algorithm

  • Gini Impurity or Entropy?

  • Regularization Hyperparameters

  • Instability

  • Overview & Applications

  • Types of Unsupervised Learning

  • Overview

  • Hierarchical Clustering

  • Ensemble Learning

  • Bagging and Pasting with Scikit-Learn

  • Out-of-Bag Evaluation

  • Random Patches and Random Subspaces

  • Random Forests

  • AdaBoost

  • Gradient Boosting

  • XGBoost & Parameters

  • Model Selection

  • Overview of Time Series Modeling

  • Time Series Patterns & Types

  • White Noise

  • Stationarity

  • Removal of Non-Stationarity

  • Time Series Models

  • Purpose of Recommender Systems

  • Paradigms of Recommender Systems

  • Collaborative Filtering

  • Association Rule Mining

  • Text Mining: Overview & Significance

  • Application

  • Text Extraction & Preprocessing

Unit 6 Week 16-20

  • Introduction

  • Why Tensorflow

  • Installation

  • Node Value

  • Linear Regression with Tensorflow

  • Gradient Descent

  • Training Machine Learning Program with Tensorflow API

  • Training Deep Neural Nets

  • Distributing Tensorflow across servers

  • Convolutional Neural Network

  • Recurrent Neural Network

  • Transfer Learning

  • Autoincoders

  • Reinforcement Learning

Unit Week - 21-25

  • Quantitative Reasoning

  • Logical Reasoning 

  •  

    Critical Reasoning

  • Integrated Reasoning

  • Verbal Reasoning

  •  Integrated Reasoning

40 Hours Of Aptitude

  • ​How to build Resume

  • How to write Cover Letter

  • LinkedIn Profile Building Session

  • Git Hub Profile Making

  • How to do Job Application 

  • How to give PPT Presentation

  • Telephonic Etiquette

  • Email Etiquette

  • Mock Interviews

20 Hours of Soft Skills Training

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Training from highly experienced instructors.

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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 

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Learn over 10 industry-relevant tools.

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

Course Details

Data Science Course with Tableau
Data Science Course with Python
Data Science Course with TensorFlow

Get Certified in 6 Months!

Start Learning Today!

  • Globally recognized certification from SkillEnable.

  • Earn between 4 LPA- 18 LPA.

  • 100% live online Training.

  • Taught by 15+ years of experienced Industry Experts.

  • Apply your skills with hands-on projects & Case Studies.

  • No prior coding experience is required to do this course. 

  • Industry Approved Certificate upon completion.

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

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