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Learn Data Science Now & Pay as you Earn!!


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 Science Involve?

  • Tools of data Science?

  • What is Machine Learning?

  • Where is Machine Learning used?

  • Job Roles

Unit 1 

Excel Introduction

  • Basic Excel & Excel interface

  • Read data in excel & shortcuts

Basic Introduction Of Tableau

         Introduction to Tableau & Installation

  • Versions of Tableau & its' Utility

  • Importing Data & User Interface

  • Dimension & Measure

  • Introduction to Tableau Commands

Basic Visualizations

  • 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

Analytics in Tableau

  • 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

Tableau Dashboard

  • Create Dashboard

  • Design Dashboard

  • Adding Filters in Tableau Dashboard

  • Tableau Workbook

  • Reporting Tools

  • Story with Dashboard

Conditional Formatting

  • Interactive Filter

  • Data Source Filter

  • Parameter Filter

Connecting Data

  • 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

Tableau Prep

  • Introduction to Tableau Prep

  • Data Connections

Case Studies

  • 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

  • Introduction to SQL Server

  • Oracle Vs. Microsoft SQL Server

  • Installing and Configuring SQL Server

  • MS Server Management Studio Installation

Databases

  • Working with Databases and Storage

  • Planning and Implementing a Backup Strategy

  • SQL Server

  • 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

T-SQL

  • 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

Miscellaneous

Normalizations

  • Treiggers

  • ER Diagrams in SQL Server

  • Industry based Projects

Unit 3 Week 04-07

Probability

  • Introduction to probability

  • Sampling and sampling distributions

  • Unions of Events and Addition Rules

  • Unions of Events and Addition Rules

  • Conditional Probability

  • Independence

  • Random Variables

Descriptive Statistics

  • Baye'S Theorem

  • Average, Mean, Median, Mode

  • Range, Quartiles & Percentiles

  • Interquartiles Range

  • R-SQUARED

Inferential Statistics

  • 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

Python Basics

  • Introduction to Python

  • Computer Programming & Data Types

  • Expressions & Variables

  • Strings & String Operations

  • Boolean Values

Python Data Structures

  • Lists

  • Tuples

  • Dictionary

  • Sets

Decision Making

  • Operators

  • Conditional Expressions

  • Loops

  • Logical and Bitwise Operations

Functions

  • Functions & Recursions

  • Special functions: Lambda, Map..

Object Oriented Programming

  • Classes & Objects

  • Instances & Attributes

  • Pre-defined functions

  • Inheritance

  • Build-in class Attributes

Exceptions & File Handling

  • Exceptions handling

  • File Handling

  • Read & Write File

Application Program Interfaces (API )

  • Simple API

  • Request

  • We scrapping

Unit 5 Week 11 - 15

Python libraries introduction

  • Virtual environment & Python libraries

  • Ipython

  • Jupyter

NumPy

  • 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

  • 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

Matplotlib

  • 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

Seaborn

  • Visualization with Seaborn

  • Matplotlib Vs. Seaborn

Unit 5 Week 11 - 15

Introduction to Machine Learning

  • Introduction

  • Types of Machine Learning

  • Supervised Vs. Unsupervised Learning

  • Instance-Based Vs. Model-Based Learning

Main Challenges in Machine Learning

  • Insufficient quantity of Training Data

  • Nonrepresentative Training Data

  • Poor-Quality Data

  • Irrelevant Features

  • Overfitting the Training Data

  • Underfitting the Training Data

Dimensionality Reduction

  • Projection & Manifold Learning

  • Principal Component Analysis

  • Data wrangling

Supervised Learning

  • Supervised Learning & Types

  • Classification & Types

  • Regression & Types

  • Accuracy Metrics

Classification

  • Training a Binary Classifier

  • Performance Measures

  • Measuring Accuracy Using Cross-Validation

  • Confusion Matrix

  • Precision and Recall

  • Multiclass Classification

  • Error Analysis

  • Multilabel Classification

  • Multioutput Classification

Linear Regression

  • Gradient Descent

  • Polynomial Regression

  • Learning Curves

  • Ridge, Lasso Regression

  • Elastic Net & Early Stopping

Logistic Regression

  • Logistic Regression

  • Training and Cost Function

  • Sigmoid Probability

  • Accuracy Matrix

  • Decision Boundaries

Support Vector Machines

  • Linear SVM & Classification

  • SVM : Linear Separability

  • SVM : Mathematical Representation

  • Nonlinear SVM & Classification

  • SVM Regression

  • Kernal Trick

Decision Trees

  • 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

Unsupervised Learning

  • Overview & Applications

  • Types of Unsupervised Learning

Clustering

  • Overview

  • K Means

  • Hierarchical Clustering

  • DBSCAN

Ensemble Learning and Random Forests

  • 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

Time Series Modeling

  • Overview of Time Series Modeling

  • Time Series Patterns & Types

  • White Noise

  • Stationarity

Removal of Non-Stationarity

  • Time Series Models

  • Recommender Systems

  • Purpose of Recommender Systems

  • Content-based Recommender Systems

  • Collaborative Filtering

  • Association Rule Mining

Text 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

R Programming

  • Introduction to R

  • Why R

  • R Vs Python

  • Download & Install Rstudio

Library/Packages

  • Hadoop

  • Install new packages

  • Load packages to library

Basics

  • Intro R Command Prompt & Script File

  • Basic Syntax

  • Data Types

  • Data Structures

  • Variables

  • Various Keywords

Operators

  • Arithmetic Operators

  • Relational Operators

  • Logical Operators

  • Assignment Operators

  • Miscellaneous Operators

Control Statements

  • If Statement

  • If-else Statement

  • else if Statements

  • R Switch Statements

  • R Break Statement

Loops

  • For loops

  • While Loop

  • Repeat Loop

Functions

  • Functions

  • Built-in Functions

  • Function Arguments

  • Types of Functions

  • Recursion

  • Conversion Functions for Data Types

  • Conversion Functions for Data Structures

Data Structures

  • Strings

  • Vectors

  • Lists

  • Arrays

  • Matrices

  • Factors

  • Data Reshaping

  • Joining Columns and Rows in a Data Frame

  • Merging Data Frames

  • Merging Data Frames

  • Data Frame

Object Oriented Programming

  • Classes & Object

  • Encapsulation

  • Polymophism

  • Inheritence

  • Abstraction

  • Looping function for objects

  • Explicit coercion

Error Handling

  • Handling Errors in R

  • Condition Handling

  • Debugging

File Handling

  • Read File

  • Write File

  • Binary Files

Data Interface

  • working with csv file

  • Working with Excel File

  • Working with Binary Files

  • Working with Json File

  • Working with XML File

  • Working with Database

Data Visualizations

  • Data visualization

  • Line Chart

  • Bar chart

  • Histograms

  • Scatter plots

  • Pie chart

  • Boxplots

Statistics with R

  • Mean, Median and Mode

  • Average, Variance and Standard Deviation

  • measure of central tendency & Measure of variability

  • Normal Distribution

  • Binomial Distribution

  • ANOVA

  • Covariance & Correlation

  • Skewness

  • Kurtosis

  • Hypothesis Testinf

  • Time Series Analysis

Machine Learning with R

  • Introduction to Machine Learning

  • Supervised Vs Unsupervised Learning

  • Regression & Its Types

  • Classification

  • Naïve Bayes classifier

  • KNN

  • Clustering

  • Decision Tree

  • Random Forest

  • Hierarchical Clustering

  • DBScan Clustering

  • Deep Learning with R

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

Apply for our Deep Dive in Data Science AI & ML Course.

Appear for your Data Science Aptitude test followed by a Personal Interview.

Selected Students will receive the ISA Track (a simplified version of the ISA Agreement) along with their Minimum Guaranteed CTC amount.

Students will have to pay a registration deposit of RS 10,000 + GST (Fully Refundable after ISA Obligation is fulfilled)

Apply for the Premium Data Science Program

Start your Data Science Training

Train with us 24 weeks on 10 industry-relevant tools.

Prepare for Mock Interviews and Placement Process.

Intern for 3 months from week 13- 24 as a part of your course amongst our 500+ Hiring Partners to gain relevant industry experience.

SkillEnable's Placement Fair

Tool focused interview preparation every weekend during the course.

Resume building, GitHub profile building, Cover Letter Preparation, Presentation skills, Business Communication Skills Training.

Personal Placement Mentor for each student.

Get referred to our 500+ Hiring Partners.

Land your Dream Job

Work with the best companies in the country

Candidates with 85% + will only be eligible for the ISA mode of payment

Program Road Map!(For ISA)

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Training from highly experienced instructors.

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100% live online learning.

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Get industry-endorsed certification from SkillEnable 

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

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Active job referrals from 500+ Fortune Companies.

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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

Why Income Share Agreement is Unique & Helpful?

  • ISA  is an arrangement where you get an opportunity to Learn now and pay the full course fee only after you start earning a sustainable amount of income which is the minimum guaranteed CTC for us. 

​​

  • ISA saves you from worrying about traditional education loans or using your savings and getting trapped in debts, in case, you do not get a high-paying job.

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  • You do not have to pay us anything if you do not start earning with a minimum guaranteed CTC.

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  • Course fee payment is directly linked to the outcome of the course.

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Get Certified in 6 Months!

Start Learning Today!

  • Globally recognized certification from SkillEnable.

  • Pay 0 tuition fee until you are placed with a Minimum Guaranteed CTC.

  • 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.

Unbeaten Salary

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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Rising Demand

With ever-evolving consumption patterns and changing market scenarios - Companies are rapidly putting AI and ML at the core of their business and operation.

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According to LinkedIn In 2020, Analytical, Data-Driven Skills like Artificial Intelligence reign supreme.

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Evolving Discipline

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

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Why Data Science Training? 

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Top Companies that Hire From Us!

SkillEnable has 500+ Hiring partners who have hired our students for roles like Business Analyst, Data Analyst, Data Scientist, Data Associate, Python & ML Expert, Subject Matter Expert in Data Science, Product Managers, Junior Manager, Financial Analyst & Quality Analyst.

Join #1 Data Science Training & Placement Institution in India

 

SkillEnable's Minimum Guarantees:

 

What's better than guaranteed success?

 

At SkillEnable, promises come before payments. We charge fees only when you start earning equal to or more than the Minimum guaranteed CTC.

For Freshers

  • 4LPA - 18LPA

For Working Professionals

  • 10% - 25% hike or between 4LPA- 8LPA whichever is maximum

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Program Features!

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Registration & ISA Signing

Students selected by a final interview by experts will sign the ISA and start the course.

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Online Assessment & Interview

Students will go through an assessment on Quantitative, Critical Reasoning, and Interpersonal skills followed by an Interview.

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Application

Filling an extensive application forms with academic, professional  and personal background.

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Document Verification 

Students will have to submit documents for verification for their credit profiling.

Our Selection Process

It's an Intensive and Elite 6 Months Program with an Extensive Selection Process.

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