We also Offer:

1. Academic projects + Job training for B.Tech 2nd year, 3rd year and final year Students on Data Science/Machine Learning/Artificial intelligence.
2. Corporate workshops & Seminars on Data Science, AI, ML, Python etc.(Check out our latest Workshop at BHEL, Hyderabad)
3. Study MS in Data Science, Artificial intelligence in UK, Germany, and Canada. For details Get in touch with our study abroad team here
4. Hackathons on Data Science, AI,ML
5. All Day In-House Data Science & AI team available for doubt sessions.
6. Hyderabad’s #1 Company Owned Training Center for Data Science & AI.

I. How to Be Successful with this Course

• Train Your Brain
• Methodology to Understand the Concepts Faster and Not Forget
• Prepare Your Own PDF Material from IPython Notebooks
• Plagiarism
• Saving Your Work
• Error Debugging

Module 1 – Introduction to course

• Introduction to corporate training
• Software life cycle
• Methodologies
• Soft skills
• Professional Ethics
• Make you strength
• Where I Stands?
  

  Module 2 – Data science introduction

• What is data science
• Need for data science?
• Data science vs Business Intelligence
• Prerequisite for learning data science
• What does a data scientist do?
• Data science life cycle with example
• Demand for data science
  

  Module 3 – Installations

• Installations required for data science

Module 4 – R Programming

• R Basics, background
• Comprehensive R Archive Network
• Demo of Installing R On windows from CRAN Website
• Installing R Studios on Windows OS
• Setting Up R Workspace.
• Getting Help for R-How to use help system
• Installing Packages – Loading And Unloading Packages
  

  Getting familiar with basics

• Operators in R – Arithmetic,Relational,Logical and Assignment Operators
• Variables,Types Of Variables, Using variables Conditional statements, ifelse(), switch
• Loops: For Loops,While Loops,Using Break statement,Switch
  

  The R Programming Language – Data Types

• Creating data objects from the keyword.
• How to make different type of data objects.
• Types of data structures in R
• Arrays And Lists- Create Access the elements
• Vectors – Create Vectors,Vectorized Operations,Power of Vectorized Operations Matrices-
  

  Building the first matrices,Matrix Operations, Subsetting, visualising subset

• Data Frames- create and filter data frames,Building And Merging data frames.

Functions And Importing data into R

• Function Overview – Naming Guidelines
• Arguments Matching,Function with Multiple Arguments
• Additional Arguments using Ellipsis,Lazy Evaluation Multiple Return Values Function as
  

  Objects, Anonymous Functions

• Importing and exporting Data into R- importing from files like excel,csv and minitab.
• Import from URL and excel Files
• Import from database.
  

  Data Descriptive

• Statistics, Tabulation, Distribution
• Summary Statistics for Matrix Objects. apply() Command. Converting an Object into a Table
• Histograms, Stem and Leaf Plot, Density
• Normal Distribution
  

  Graphics in R – Types of graphics

• Bar Chart, Pie Chart, Histograms- Create and edit.
• Box Plots- Basics of Boxplots- Create and Edit Visualisation in R using ggplot2.
• More About Graphs: Adding Legends to Graphs,Adding Text to Graphs, Orienting the AxisLabel.
  

  Module 5 – Python programming

• Introduction to python
• Operators
• Data Types
• Control Statements
• Functions
• Data Structures – Lists, Sets, Tuples, Strings, Dictionaries,
• OOPS Concept
  

  Module 6 – Libraries

  • Numpy
  • Pandas
  • Scipy
  • Scikit-Learn • Keras

  • Matplotlib • Seaborn
  • Cufflinks • NLTK

  Note: Installations and work end to end. As per requirements going to work with different libraries.

Module 7 – Data Exploration

• Collecting data from different sources
• Analyzing data
• Data preprocessing
• Data munging
• Data mining
• Data manipulation
• Data visualization
• Feature Selection
• Feature Scaling
• Dimensionality reduction
  

  Module 8 – Statistics

• Basics of Statistics
• Descriptive Statistics
• Inferential Statistics
• Qualitative vs. Quantitative Analysis
• Hypothesis Testing
• Data Distribution
  

  Module 9 – Other Mathematics Concepts

• Probability
• Calculus
• Linear algebra
  

  Module 10 – Machine Learning

• Introducing Machine Learning models
• Supervised learning
• Regression and Classification models
• Unsupervised learning
• Clustering and Aggregation models
• Semi supervised learning
• Over fitting and under fitting(Linear, Logistic, Navi Bayes, K-Nearest Neighbors, Support Vector Machine, Decision Trees, Random Forest …..)Note: Deals with Mathematics and Programming part
  

  Module 11 – Deep Learning and other algorithms

• Introduction to Deep learning
• OpenCV
• Artificial Neural Networks
• Convolutional Neural Networks
• Recurrent Neural Networks
• Tensor flow
• Over fitting and under fitting

Module 12 – Natural Language Processing

• Introduction to NLP
• Text Preprocessing
• Lemmatization
• Stemming
• Text to features
• Important Tasks in NLP
• Important Libraries for NLP
  

  Module 13 – Computer Vision

  WORKING ON DATA SCIENCE PROJECTS

• Examine your problem
• Prepare your data (raw data, feature extraction, feature engineering, etc.)
• Spot-check a set of algorithms
• Examine your results
• Double-down on the algorithms that worked best Note: Deals ‘n’ number of real time projects WORK ENVIRONMENT

• • Status meetings
  • Meetings with stockholders
  • Participating in gathering requirements
  • Working on client projects
  • Coordinating team

System Requirements

• Ram 4GB Min
• Hard Disk 250GB
• Processor i3 Min

II. Monthly Tests

III. Mock interviews

IV. Certifications

Course Highlights

1. One-on-One Dedicated Mentorship
2. Personal guidance throughout the journey
3. Advanced job ready curriculum
4. Curriculum curated with help of Top IITs & IIM’s
5. Weekly Assignments
6. Timely doubt sessions
7. Certification from Top MNCs
8. Job placement assistance
9. Special info sessions by industry leaders
10. Monthly review tests & personal support
11. 70% Practical & 30% Theoretical Curriculum

• 100+ Data Science companies in India 2021

• 2020 – Year of AI | Telangana Government declared 2020 as Year of Artificial Intelligence

• How to Build a Career in AI and Machine Learning (Data Science)?

• 5 Steps to Landing Your Dream Job as a Data Scientist

• Difference Between Artificial Intelligence-Machine Learning and Deep Learning?