Data Science Training Institute in Marathahalli

This document is intended to provide information on Certified Data Scientist (CDS-DS1050) certification for registered training providers to structure the course curriculum as per IABAC syllabus guidelines and for individuals, who are preparing for IABAC CDS certification exam.

• Introduction to Data Science with Python
• Python Basics: Basic Syntax, Data Structures
• Data objects, Math, Comparison Operators, Condition Statements, loops, lists, tuples, dicts,
  functions
• Numpy Package
• Pandas Package
• Python Advanced: Data Mugging with Pandas
• Python Advanced: Visualization with Matplotlib
• Exploratory Data Analysis: Data Cleaning, Data Wrangling
• Exploratory Data Analysis: Case Study

• Introduction to Statistics
• Harnessing Data
• Exploratory Analysis
• Distributions
• Hypothesis & Computational Techniques
• Correlation & Regression

• Visual Analytics Basics
• Basic Charts, Plots

• Install SQL packages and Connecting to DB
• RDBMS (Relational Database Management) Basics
• Basics of SQL DB, Primary key, Foreign Key
• SELECT SQL command, WHERE Condition
• Retrieving Data with SELECT SQL command and WHERE Condition to Pandas Dataframe.
• SQL JOINs
• Left Join, Right Joins, Multiple Joins

• Machine Learning Introduction
• What is ML? ML vs AI. ML Workflow, Statistical Modelling of ML. Application of ML
• Machine Learning Algorithms
• Popular ML algorithms, Clustering, Classification and Regression, Supervised vs Unsupervised.
• Choice of ML
• Supervised Learning
• Simple and Multiple Linear Regression, KNN, and more
• Linear Regression and Logistic Regression
• Theory of Linear regression, hands on with use cases
• K-Nearest Neighbour (KNN)
• Decision Tree
• Naïve Bayes Classifier
• Unsupervised Learning: K-Means Clustering

• Advanced Machine Learning Concepts
• Tuning with Hyper parameters
• Random Forest – Ensemble
• Ensemble Theory, Random Forest Tuning
• Support Vector Machine (SVM)
• Simple and Multiple Linear Regression, KNN
• Natural Language Processing (NLP)
• Text Processing with Vectorization, Sentiment Analysis with Text Blob, Twitter Sentiment
  Analysis.
• Naïve Bayes Classifier
• Naïve Bayes for Text Classification, New Articles Tagging
• Artificial Neural Network (ANN)
• Basic ANN network for Regression and Classification
• TensorFlow Overview
• Deep Learning Intro

• Word2Vector.
• Named Entity Recognition(NER).
• Coreference Resolution.
• Semantic Network.
• Topic Modelling, TF-IDF, POS Tagging.
• Regex for NLP, Text Similarity.
• Context Understanding Using Bert.
• Handling Audio Data, Text to Audio, Audio to Text.

• Basics of Application Program Interface (API)
• API basics, loosely Coupled Architecture
• Installing Flask
• Installation and configuring Flask and cross domain authentication
• End to End ML project with API Deployment
• Complete Project Flow with API Deployment and assessing through the website