Instructor Led Live Online
Self Learning + Live Mentoring
In - Person Classroom Training
The entire training includes real-world projects and highly valuable case studies.
IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.
The following topics are covered here
Module 1 - Introduction to Data Science with Python
Module 2 - Python Basics: Basic Syntax, Data Structures
Module 3 - Numppy Package
Module 4 - Pandas Package
Module 5 - Python Advanced: Data Mugging with Pandas
Module 6 - Python Advanced: Visualization with MatPlotLib
Module 7 - Exploratory Data Analysis: Case Study
The following topics are covered here
Module 1: Introduction to Statistics
Module 2: Harnessing Data
Module 3: Exploratory Analysis
Module 4: Distributions
Module 5: Hypothesis & computational Techniques
Module 6: Correlation & Regression
The following topics are covered here
Module 1: Machine Learning Introduction
Module 2: Machine Learning Algorithms
Module 3: Supervised Learning
Module 4: Unsupervised Learning
The following topics are covered here
Module 1: Advanced Machine Learning Concepts
Module 2: Principle Component Analysis (PCA)
Module 3: Random Forest - Ensemble
Module 4: Support Vector Machine (SVM)
Module 5: Natural Language Processing (NLP)
Module 6: Naïve Bayes Classifier
Module 7: Artificial Neural Network (ANN)
Module 8: Tensorflow overview and Deep Learning Intro
Module 1: Tableau Introduction
Module 2: Connecting to Data Source
Module 3: Visual Analytics
Module 4: Forecasting
Module 1: Understanding Business Case
Module 2: Writing Data Science Business Case
Module 3: Benefits Analysis
Module 4: Starting project, Setting up Team and closing
Introduction to Data Science
What is Data Science?
What is Machine Learning?
What is Deep Learning?
What is Artificial Intelligence?
Data Analytics and its types
Introduction to R
What is R?
Why R?
Installing R
R environment
How to get help in R
R Studio Review
R Packages
Data Types
Variable Vectors
Lists
Environment Setup
Array
Matrix
Data Frames
Factors
Loops
Functions
Packages
In-Built Datasets
R Basics
Importing data
Manipulating data
Statistics Basics
Error metrics
Machine Learning
Supervised Learning
Unsupervised Learning
Machine Learning using R
Introduction to Deep Learning
Overview of Machine Learning Concepts
TensorFlow Essentials
ML Algorithm - Linear Regression in TensorFlow
Deep Neural Networks in TensorFlow
Convolutional Neural Networks
Reinforcement Learning in Tensorflow
Hands on Deep Learning Application with TensorFlow
Introduction to TensorFlow
Basic Statistics
Machine Learning Introduction
TensorFlow Essentials
ML Algorithm - Linear Regression in TensorFlow
ML Algorithm - Classification in TensorFlow
ML Algorithm - Clustering in TensorFlow
Simple Neural Networks in TensorFlow
Reinforcement learning
Convolutional and Recurrent Neural Networks
Case study - Stock Market Analsis with TensorFlow
The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -
The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.
No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.