Introduction to Data Science

• Why Data Science?
• What is Data Science?
• Components of Data Science
• Life Cycle of Data Science
• Tasks of Data Scientist
• Data Science in different sectors
• Data Science tools

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The syllabus is divided into 4 modules where you will learn about various Data Science skills using Python programming language. In the first module, Basic and advanced Python will be covered. In the second module, you will learn about Data Analytics frameworks using Numpy, Pandas, etc. In the third module, we will be covering data visualization which is necessary for data science. In the last modules, Machine Learning and its various algorithms will be covered.

Module 1 

Python (Basic & Advanced)

Python Basic

Introduction to Python

• Overview of Python
  • Introduction of Python
  • Installation of python
  • Feature of python
  • History of python
  • Installing Python and code editor IDE
  • Introduction to the anaconda distribution platform
  • Introduction to Jupytrt Notebook and how to work on it
  • Introduction to google collaboratory
  • Running python programs using terminal
  • Variables
  • Data type
  • Working on Integers, Floats, Booleans in Python
  • Playing with Strings in Python
  • Working on Lists
    • Creating lists
    • List functions
    • List Methods
    • Nested Lists
  • Working on Tuples
    • Creating Tuple
    • Tuple Functions
    • Tuple Methods
  • Working on Sets
    • Creating Sets
    • Set Functions
    • Set Methods
    • Frozen Sets
  • Working on Dictionary
    • Creating Dictionaries
    • Dictionary Functions
    • Dictionary Methods
  • Operators
  • Arithmetic Operator
  • Relational operators
  • Assignment operators
  • Logical operators
  • Special operators
  • Bitwise Operators
  • Control Structures
    • Conditional Statements
    • IF STATEMENT
    • IF / ELSE Statement
    • IF/ElIF/ELSE Statement
    • Nested IF/ELSE Statements
  • Expressions and Statements
  • Indentations
  • Writing Comments
  • Iterations and Iterables
  • Loops
    • For Loop
    • While Loop
    • Range and xrange Functions
    • Break, Continue, and Pass
  • Python Functions
    • Introduction to Python function
    • Types of Python Functions
    • Built-in Python Functions
    • User-defined Python function
    • Defining function
    • Calling Function
    • Parameters and arguments
    • Positional Arguments
    • Default Arguments
    • Keyword Arguments
    • Arbitrary Arguments
    • Docstrings
    • Recursive Functions
    • Lamda Functions
  • Object Oriented Programming
    • Objects
    • Classes
    • Creating empty class
    • Creating class and defining attributes
    • Class methods and attributes
    • Constructors
    • Generators
    • Decorators
    • Instance and class variables
    • Inheritance
    • Polymorphism
  • File Handling
    • Reading and writing files
    • Opening and closing files
    • Deleting Testing and debugging Files
    • Errors and Exception Handling
  • Modules and Packages
    • Importing Modules
    • Creating and importing packages
    • Namespaces
  • Testing and Debugging
    • Debugging with print statement
    • Debugging with the debugger
    • Testing with unit Test module
  • Database connectivity

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Data Analytics Training with Tableau and PowerBI

Module 2

Basic Excel Course Syllabus Introduction to Computers

• Font formatting
• Number formatting
• Table formatting
• Conditional formatting
• Hide/ Unhide
• Sort/filter
• Paste special
• Find and select

Insert

• Illustrations
• Charts
• Tex

Basic Functions

• Sum/Average/Count/Max/Min
• Basic Text/date/time/lookup/information functions
• Name manager
• Formula Auditing

Data

• Import from web
• Import from text
• Text to columns
• Remove duplicates
• Grouping and ungrouping

Review

• Proofing
• Comments
• Protection

Views

• Typesofviews
• Zoom
• Windows

Developer

• Enable developer
• Using checkbox/option buttons

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Module 3

Advanced Excel Course Syllabus

Excel 2007 & 2010,2013, 2016 Quick Overview

• Difference between Excel2003, 2007 and 2010, 2013, 2016
• Use of Excel, its boundaries & features

Basic Formula

• Formulae that Add/ Subtract/ Multiply/ Divide
• BODMAS / Formula Error Checking
• The Sum Function

Absolute Referencing

• Problems with Absolute /Relative Cell Referencing
• Creating Absolute /Mixed References

LOOKUP Functions

• The VLOOKUP/ HLOOKUP Functions

Pivot Tables

• Creating,Formatting Simple Pivot Tables
• Page Field in a Pivot Table
• Formatting a Pivot Table
• Creating/ Modifying a Pivot Chart

Logical Functions

• IFs and Nested IF Functions
• Using AND/OR/ NOT Functions

Statistical Functions

• Using The SUM IF / COUNT IF Functions
• Using The AVERAGE/COUNT/LARGER/SMALLER Functions

Pivot Tables–Advance

• Adding new calculated Fields/Items
• Changing the Summary Function
• Consolidate Pivot table

LOOKUP Functions–Advance

• MATCH with VLOOKUP Functions
• INDEX& MATCH Functions
• OFFSET/INDIRECT functions

Logical Functions–Advance

• If Loop and Nested IF Loop Functions
• Using IF/IS ERROR Functions

Chart Data Techniques

• The Chart Wizard
• Chart Types
• Adding Title/Legends/ Labels
• Printing Charts
• Adding Data to a Chart
• Formatting/Renaming/Deleting Data Series
• Changing the Order of Data Series

Date / Time Functions

• Using The Today
• Now& Date Functions
• Using The Dated if/ Network days/Eomonth Functions
• Using The Week num Functions
• Using The Edate/Network days.Intl/Weekdays.Intl Functions

Text Functions Using

• The Mid/Search/Left/Right Functions
• Using The Trim/Clean/Upper/Lower Functions
• Using The Substitute/Text Functions
• Using The Trim/Clean/Proper/Dollar Function

Validations

• Input Messages/Error Alerts/Drop-Down Lists
• Conditional Formatting

Advanced Filters

• Extracting Records with Advanced Filter
• Using Formula sin Criteria

Advanced Sorting

• Sorting by Top to Bottom/Left to Right
• Creating/Deleting Custom List
• Sort by using Custom List

Hyper/Data Linking

• Hyper linking data,within sheet/workbook
• Linking&Updating links between workbooks&application

Math & Trigonometry Functions

• Using SUM PRODUCT Functions
• Using FLOOR/CEILING/MROUND/MOD/QUOTIENT Functions

Summarizing Data

• Creating Subtotals/Nested Subtotals
• SUBTOTALS Formula

Outlining

• Creating/Working with an Automatic/Manual Outline
• Grouping/Un grouping

Consolidation

• Consolidating Data with Identical/Different Layout

Using Auditing Tools

• Displaying/Removing Dependent&Precedent Arrows
• Evaluate Formula–StepIN/StepOut

Custom Views

• Creating Custom Views
• Displaying Custom Views
• Deleting Custom Views

Sharing and Protecting Workbooks

• Sharing Workbooks&Tracking Changes
• Protecting sheets/workbooks/Files

Importing & Exporting Data

• Importing Data from Database/ TextFiles/Web
• Exporting Data
• Changing External Data Range

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Module 4

SQL

 Introduction of SQL

SQL Statements

• DDL
• DML
• DQL
• DCL

SQL CONSTRINTS

• UNIQUE
• PRIMARY KEY
• NULL
• NOT NULL
• CHECK
• DEFAULT
• FOREIGN KEY

SQL OPERTORS

• AND
• OR
• LIKE
• IN
• BETWEEN
• IS

SQL FUNCTIONS

• MIN()
• MAX()
• COUNT()
• SUM()
• AVG()
• LENGTH()
• REVERSE()
• REPLACE()
• INSTR()
• SUBSTR()
• TRIM()
• ROUND()
• ABS()
• SQRT()
• MOD()

SQL CLAUSE

• WHERE
• GROUP BY
• ORDER BY
• HAVING

SUB QUERIS

JOINS

• inner join
• outer join
• left join
• right join

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Module 5

TABLEAU

Understanding Data

• What is data Where to find data
• Foundations for building Data Visualizations

Creating Your First visualization

• Getting started with Tableau Software Using Data file formats
• Connecting your Data to Tableau
• Creating basic charts(line,bar charts,Tree maps) Using the Show me panel

Tableau Calculations

• Overview of SUM, AVR, and Aggregate features
• Creating custom calculations and fields
• Applying new data calculations to your visualization

Formatting

• Visualizations
• Formatting Tools and Menus
• Formatting specific parts of the view
• Editing and Formatting Axes

Manipulating Data in Tableau

• Cleaning- up the data with the Data Interpreter Structuring your data
• Sorting and filtering Tableau data
• Pivoting Tableau data

Advanced Visualization Tools

• Using Filters
•  Using the Detail panel Using the Size panels Customizing filters
• Using and Customizing tooltips Formatting your data with colors

Creating Dashboards & Stories

• Using Storytelling
• Creating your first dashboard and Story Design for different displays
• Adding interactivity to your Dashboard

Distributing & Publishing Your Visualization

• Tableau file types
• Publishing toTableau
• Online Sharing your visualization Printing and exporting

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Module 6

POWER BI

Power BI Introduction

• Data Visualization,Reporting
• Business Intelligence(BI),Traditional BI,Self-Serviced BI
• Cloud Based BI,On Premise BI
• Power BI Products
• Power BI Desktop(Power Query,Power Pivot,Power View)
• Flow of Working PowerBI Desktop
• PowerBI Report Server,PowerBI Service,PowerBI Mobile Flowof Working PowerBI/PowerBI Architecture
• A Brief History of PowerBI
• Data Transformation,Benefits of Data Transformation
• Shape or Transform Data using Power Query

Power Query

• Overview of Power Query/Query Editor, Query Editor User Interface The
• Ribbon(Home,Transform, Add Column,View Tabs)
• The Queries Pane,The Data View/Results Pane,The Query Settings Pane,Formula Bar
• Saving the Work
• Datatypes,Changing the Data type of a Column Filter in Power Query
• Auto Filter/Basic Filtering
• Filter a Column using Text Filters
• Filter a Column using Number Filters
• Filter a Column using Date Filters
• Filter Multiple Columns
• Remove Columns/Remove Other Columns
• Name/Rename a Column
• Reorder Columns or Sort Columns
• Add Column/Custom Column Split
• Columns
• Merge Columns
• PIVOT,UNPIVOT Columns
• Transpose Columns
• Header Row or Use First Row as Headers
• Keep Top Rows,Keep Bottom Rows Keep
• Range of Rows
• Keep Duplicates,Keep Errors
• Remove Top Rows, Remove Bottom Rows,Remove Alternative Rows
• Remove Duplicates,Remove Blank Rows,Remove Errors
• Group Rows/Group By

Visualizations

• • Visualizing Data,Why Visualizations
  • Visualization types,Create and Format Bar and Column Charts
  • Create and Format Stacked Bar Chart Stacked Column Chart Create
  • and Format Clustered Bar Chart,Clustered Column Chart
  • Create and Format 100% Stacked Bar Chart,100% Stacked Column Chart Createand
  • Format Pie and Donut Charts
  • Create and Format Scatter Charts
  • Create and Format Table Visual,Matrix Visualization
  • Line and Area Charts
  • Create and Format Line Chart,Area Chart,Stacked Area Chart
  • Combo Charts
  • Create and Format Line and Stacked Column Chart,Line and Clustered Column Chart
  • Create and Format Ribbon Chart,Waterfall Chart,Funnel Chart

PowerBI Service

• PowerBI Service Introduction,PowerBI Cloud Architecture
• Creating PowerBI Service Account, SIGN IN to PowerBI Service Account
• Publishing Reports to the PowerBI service,Import/Getting the Report to PBI Service My
• Workspace/App Workspaces Tabs
• DATASETS,WORKBOOKS,REPORTS,DASHBOARDS
• Working with Datasets,Creating Reports in Cloud using Published Datasets

Creating Dashboards

• P in Visuals and Pin LIVE Report Pages to Dashboard
• Advantages of Dashboards
• Interacting with Dashboards
• Formatting Dashboard
• Sharing Dashboard

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Module 7

Mathmatics

• Calculus
• Vector Algebra
• Matrices Algebra
• Introduction to Statistics
• Descriptive Statistics
• Inferential Statistics
• Probability Theory
• Random Variables
• Discrete & Continuous Distribution
• Joint Distribution
• Central Limit Theorem
• Hypothesis Testing
• Statistics Interview Questions

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Module 8

Machine Learning

• Introduction to ML
• Supervised and Unsupervised Machine Learning
• Linear Regression & Linear Regression Implementation
• Bias Variance Tradeoff
• Cross Validation and Hyper Parameter Tuning
• Logistic Regression
• Decision Trees and Ensembles of Decision Trees
• K-Nearest Neighbours
• Naive Bayes Classifier
• Support Vector Machine
• Principal Component Analysis
• K-Means Clustering
• Introduction to Natural Language Processing
• User-Based Collaborative Filtering
• Item-Based Collaborative Filtering
• Finding Movie Similarities
• Improving the Results of Movie Similarities
• Making Movie Recommendations to People
• Improve the recommender’s results
• Machine Learning in Healthcare
• Machine Learning in Fraud Risk Analytics
• Machine Learning in Healthcare
• Machine Learning Interview Questions

====================================================

Project: Project will be given as per latest topic.

Have a great career ahead…!!

We Will Be Updated Soon.

Machine Learning Using Python

Basic Python

1  Introduction

1.1    What is Python..?

1.2   A Brief history of Python

1.3   Installing Python

1.4   How to execute Python program

2. Using the Python Interpreter
   • 1. Invoking the Interpreter
     • 1.1. Argument Passing
     • 1.2. Interactive Mode
   • 2. The Interpreter and Its Environment
     • 2.1. Source Code Encoding

3. An Informal Introduction to Python
   • 1. Using Python as a Calculator
     • 1.1. Numbers
     • 1.2. Strings
     • 1.3. Lists
   • 2. First Steps Towards Programming

4. More Control Flow Tools
   • 1. if Statements
   • 2. for Statements
   • 3. The range() Function
   • 4. break and continue Statements, and else Clauses on Loops
   • 5. pass Statements
   • 6. Defining Functions
   • 7. More on Defining Functions
     • 7.1. Default Argument Values
     • 7.2. Keyword Arguments
     • 7.3. Arbitrary Argument Lists
     • 7.4. Unpacking Argument Lists
     • 7.5. Lambda Expressions
     • 7.6. Documentation Strings
     • 7.7. Function Annotations
   • 8. Intermezzo: Coding Style

5. Data Structures
   • 1. More on Lists
     • 1.1. Using Lists as Stacks
     • 1.2. Using Lists as Queues
     • 1.3. List Comprehensions
     • 1.4. Nested List Comprehensions
   • 2. The del statement
   • 3. Tuples and Sequences
   • 4. Sets
   • 5. Dictionaries
   • 6. Looping Techniques
   • 7. More on Conditions
   • 8. Comparing Sequences and Other Types

6. Modules
   • 1. More on Modules
     • 1.1. Executing modules as scripts
     • 1.2. The Module Search Path
     • 1.3. “Compiled” Python files
   • 2. Standard Modules
   • 3. The dir() Function
   • 4. Packages
     • 4.1. Importing * From a Package
     • 4.2. Intra-package References
     • 4.3. Packages in Multiple Directories

7. Input and Output
   • 1. Fancier Output Formatting
     • 1.1. Formatted String Literals
     • 1.2. The String format() Method
     • 1.3. Manual String Formatting
     • 1.4. Old string formatting
   • 2. Reading and Writing Files
     • 2.1. Methods of File Objects
     • 2.2. Saving structured data with json

Data Science with Python

1. Install Anaconda Distribution as per OS from https://www.anaconda.com/distribution/ (Python 3.7 version)
2. Sign Up for account creation on https://www.hackerrank.com/
3. Sign up for account creation on https://www.kaggle.com/
4. Sign up for account creation on https://github.com/
5. Git Bash Utility – https://git-scm.com/downloads

Module 1: Statistics and Probability

1. Descriptive Statistics:

• Central tendency: Mean, Median, Mode
• Sample variance
• Standard deviation
• Random Variables: Discrete, Continuous
• Probability density functions
• Binomial distribution
• Expected Value, E(X)
• Poisson Process
• Law of large numbers
• Standard normal distribution and empirical rule
• Z-score

1. Inferential Statistics:

• Central limit theorem
• Sampling distribution of the sample mean
• Standard error of the mean
• Mean and variance of Bernoulli distribution
• Margin of error 1
• Margin of error 2
• Confidence interval
• Hypothesis testing and p-value
• One-tailed and two tailed tests
• Z-statistics and T-statistics
• Type 1 error
• Squared error of regression line
• Co-efficient of determination
• Chi-square distribution
• Pearson’s chi square test (goodness of fit)
• Co-relation and casualty.

Module 2: Data Analysis using Python

1. Numpy

• Numpy Vector and Matrix
• Functions – arange(), zeros(), ones(), linspace(), eye(),
• reshape(), random(), max(), min(),
• argmax(), argmin(), shape and dtype attribute
• Indexing and Selection
• Numpy Operations – Array with Array, Array with Scalars,
• Universal Array Functions

1. Pandas

• Pandas Series
• Pandas Data-Frame
• Missing Data (Imputation)
• Group by Operations
• Merging, Joining and Concatenating Data-Frame.
• Pandas Operations
• Data Input and Output from wide variety of formats like csv, excel, db and html etc.

Module 3: Data Visualization using python Matplotlib, Seaborn, Pandas-in built, Plotly and Cufflinks

1. Matplotlib

• plot() using Functional approach
• multi-plot using subplot()
• figure() using OO API Methods
• add_axes(), set_xlabel(), set_ylabel(), set_title() Methods
• Customization – figure size, impoving dpi, Plot appearance,
• Markers, Control over axis appearance and special Plot Types

1. Seaborn

• Distribution Plots using distplot(), jointplot(), pairplot(), rugplot(), kdeplot()
• Categorical Plots using barplot(), countplot(), boxplot(), violinplot(), stripplot(), swarmplot(), factorplot()
• Matrix Plots using heatmap(), clustermap()
• Grid Plots using PairGrid(), FacetGrid()
• Regression Plots using lmplot()
• Styles and Colors customization.

1. Plotly and Cufflinks

• Interactive Plotting using Plotly and Cufflinks

1. Pandas Built-in

• Histogram, Area Plot, Bar Plot, Scatter Plot, Box-plot, Hex-plot, Kde-plot, Density Plot e. Choropleth Maps
• Interactive World Map and US Map using Plotly and Cufflinks Module

Module 4: GIT

• Distribution Version Control System
• How internally, GIT Manages Version Control on Changesets.
• Creating Repository
• Basic Commands like, git status, git add, git remove, git branch, git checkout, git log, git cat-file, git pull, git push, git commit
• Managing Configuration – System Level, User Level, Repository level

Module 5: Jupyter Notebook

• Introduction, Basic Commands, Keyboard Shortcut and Magic Functions

Module 6: Linear Algebra and Calculus

• Vector and Matrix, basic operations
• Trigonometry
• Derivatives

Module 7: SQL

• MySQL Server and Client Installation
• SQL Queries
• CRUD Operations
• Types of tables(Fact and dimension)

Module 8: Big Data

• What is big data?
• What is distributed computing?
• What is parallel processing?
• Why data scientist require big data?

Module 9: Machine Learning Introduction

• What is Machine Learning?
• Machine Learning Process Flow-Diagram
• Different Categories of Machine Leaning – Supervised, Unsupervised and Reinforcement
• Scikit-Learn Overview
• Scikit-Learn cheat-sheet

Module 10: Regression

• Linear Regression
• Robust Regression (RANSAC Algorithm)
• Exploratory Data Analysis (EDA)
• Correlation Analysis and Feature Selection
• Performance Evaluation – Residual Analysis, Mean Square Error (MSE), Co-efficient of
• Determination R^2, Mean Absolute Error (MAE), Root Mean Square Error (RMSE)
• Polynomial Regression
• Regularized Regression – Ridge, Lasso and Elastic Net Regression
• Bias-Variance Trade-Off
• Cross Validation – Hold Out and K-Fold Cross Validation
• Data Pre-Processing – Standardization, Min-Max, Normalization and Binarization
• Gradient Descent

Module 11: Classification – Logistic Regression

• Sigmoid function
• Logistic Regression learning using Stochastic Gradient Descent (SGD)
• SGDClassifier
• Measuring accuracy using Cross-Validation, Stratified k-fold
• Confusion Matrix – True Positive (TP), False Positive (FP), False
• Negative (FN), True Negative (TN)
• Precision, Recall, F1 Score, Precision/Recall Trade-Off
• Receiver Operating Characteristics (ROC) Curve.

Module 12: Classification – k-Nearest Neighbor(KNN)

• Classification and Regression
• Application, Advantages and Disadvantages
• Distance Metric – Euclidean, Manhattan, Chebyshev, Minkowski
• Measuring accuracy using Cross-Validation, Stratified k-fold, Confusion Matrix, Precision, Recall, F1-score.

Module 13: Classification – SVM (Support Vector Machine)

• Classification and Regression
• Separating line, Margin and Support Vectors
• Linear SVC Classification
• Polynomial Kernel – Kernel Trick
• Gaussian Radial Basis Function (rbf)
• Grid Search to tune hyper-parameters.
• Support Vector Regression.

Module 14: Classification –Decision Trees

• CART (Classification and Regression Tree)
• Advantages and Disadvantages and its applications.
• Decision Tree Learning algorithms – ID3, C4.5, C5.0 and CART.
• Gini Impurity, Entropy and Information Gain
• Decision Tree Regression
• Visualizing a Decision Tree using graphviz module.
• Regularization using tuning hyper-parameters using GridSearch CV.

Module 15: Classification – Ensemble Methods

• Bootstrap Aggregating or Bagging
• Random Forest algorithm
• Extremely Randomized (Extra-Trees) Ensemble
• Boosting – AdaBoost (Adaptive Boosting), Gradient Boosting
• Machine (GBM), XGBoost (Extreme Gradient Boosting)

Module 16: Unsupervised Learning – Clustering

• Connectivity- based Clustering using Hierarchical Clustering.
• Ward’s Agglomerative Hierarchical Clustering
• K-Means Clustering
• Elbow Method and Solhouette Analysis

Module 17: Unsupervised Learning – Dimensionality Reduction

• Linear Principal Component Analysis (PCA) reduction.
• Kernel PCA
• Linear Discriminant Analysis (LDA) on Supervised Data.

Module 18: Model Deployment On AWS Cloud

• What is cloud computing?
• What is AWS?
• How to store data in AWS S3?
• Create deep learning instance on EC2.
• Amazon sagemaker to train, tune, build and deploy on production.

Module 19: Tableau

• What is tableau? Its Application
• Installing tableau public
• Tableau Application and use
• Tableau tool introduction
• Tableau UI-Dimensions and measures
• Connecting to data
• Filter and its types
• Groups
• Set
• Hierarchy
• Graphs
• Table calculation
• LOD Expression
• Data Blending

• How we are Different from Others : Our Teachers covers each topics with Real Time Examples . They take 8 Real time project and more than 72+ assignments  for almost every topic. We have Trainer from Real Time Industry with 15 years experience in DS. They are working as Data Science Machine Learning and AI consultant having 10+ years in ML & AI real time implementation and migrations.

This is completely Practical oriented training , Means everything you learn you will be able to code for the same . We have students who get confident in coding within 1 week of joining the training. that is our success and method of teaching. Here in Yess InfoTech , we always take prerequisite sessions also. Also we start from basic installation of the IDEs and other required softwares. Our way of teaching is that student  will gain the confidence  that , they got up-skilled to a different level. Also our student got many great positions and salary ranges in many great organizations.

• • 5 DS Domain Based Project With Real Time Data ( with one trainer – two project.
  • 9 Moc interviews(Monthly 3)
  • Unlimited Assignments
  • 28 Real Time Scenarios and Major topics
  • Basic Python
  • Machine Learning with Python
  • Installation
  • Data Visualization in R
  • 19 Modules on Basics

60 Hours Online Sessions

12 Hours of assignments

10 hours for One Project and 50 Hrs for 2 Project ( Candidates should prepare with mentor support . 50 hours mentioned is total hours spent on project by each trainer )

Unlimited  Interview Questions

Administration and Manual Installation of python with other Domain based projects will be done on regular basis apart from our normal batch schedule .

We do take projects

• • Training By 15+ Years experienced Real Time Trainer
  • A pool of 60+ real time Practical Sessions on Data Science
  • Scenarios and Assignments to make sure you compete with current Industry standards
  • World class training methods
  • Training  until the candidate get satisfed
  • Certification and Placement Support until you get certified and placed for 4 years
  • All training in reasonable cost
  • 10000+ Satisfied candidates
  • 5000+ Placement Records
  • Corporate and Online Training in reasonable Cost
  • Complete End-to-End Project with Each Course
  • World Class Lab Facility which facilitates I3 /I5 /I7 computers
  • Wifi available in Lab

 

• • Resume And Interview preparation with 100% Hands-on Practical sessions
  • Doubt clearing sessions any time after the course till 1 year
  • Happy to help you any time after the course also

Trainer is having 15 year experience in Data Science with 10  years in Data Science Machine Learning and AI. It has been 15 years now that he has been working extensively in the top level Software company. He is having different kind of certifications in DS. He also have done corporate sessions and seminars both in India and abroad. Recently he was engaged by Yess InfoTech for sessions and professional motivator for working processionals to achieve their day to day targets.

All trainers at our organization are currently working on the technologies in reputed organization. The curriculum is not just some theory or  some PPTs. We have all practical sessions and that to we ask our student to implement the same in the session only. We provide notes for the same. We use simple easy language and the contents are well absorbed by the candidates. The always give assignment. Also that the faculties are industry experienced so we give real time projects and practice. We also provide recorded sessions but that will be costing differently. Also we provide result oriented training.