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:

  • Working Professionals 

  • Graduates

Eligibility Criteria:

  • Anyone, whether a newcomer or a professional, willing to learn Data Science can opt for it.

  • No prior coding experience required.

  • 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?

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

  • Autoencoders

  • Reinforcement Learning

Unit 7 Week 21-25

  • Sorting, Filtering

  • Pivots, Lookup function

  • Conditional Formatting

  • Logical Operators and Functions

  • Data Validation

  • Text Functions

  • Dashboard

  • Case Study

Unit 8  Week 26-30

  • VBA Basics

  • Variables

  • Conditional Logic: IF AND SELECT CASES

  • Loops

  • Cells, Rows, Columns & Sheets

  • Massage Box & Input Boxes

  • Events

  • Application Setting

  • ADVANCED PROCEDURES, VARIABLES AND FUNCTIONS

  • ARRAYS

Unit 9 Week 31-36

  • Introduction to Power BI
    -Components of Power BI
    -Connecting to data (Excel and csv)

  • Power Query Editor - Data Transformation
    -Merging and Appending Queries
    -Renaming Columns, replacing values, Column profiling, etc.
    -Adding Conditional Columns
    -Transforming Numeric, Text and Date columns

  • Data Modelling
    -Model tab
    -Managing Relationships
    -Data tab
    -DAX formulae

  • Power BI Desktop
    -Data Tab - Changing column properties
    -Report Tab - Data Visualization
    Charts in Power BI
    Matrixes and tables
    Slicers
    Map Visualizations
    Modifying colors in charts and visuals
    Shapes, text boxes, and images
    Page layout and formatting

Unit 10 Week 42-47

  • 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 fees after signing the ISA Track.

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 from 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

Program Road Map!

Learn now pay later

Training from highly experienced instructors.

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100% Live Online Learning with 1:1 Mentorship

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Get industry-endorsed certification from SkillEnable 

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280+ hours of live exhaustive training  sessions split over 24 weeks with Lifetime assess of the course materials.

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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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Course Details

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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.

​​

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

​​

  • 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 a part of the tuition fees only after you are earning with a Minimum Guaranteed CTC.

  • Earn between 6 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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Top Skill

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? 

Learn now pay later

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.

Lifetime Placement Assistance ​

We provide lifetime placement assistance to all our students . Our Virtual hiring drive gives the opportunity to interview with SkillEnable's 500+ hiring partners ensuring the career one desires.

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