CSC - MYLAPORE

OBJECTIVE :

The objective of the data scientist is to explore, sort and analyze megadata from various sources in order to take advantage of them and reach conclusions to optimize business processes or for decision support.

TARGET AUDIENCE :

students / Graduates / Diploma Students / Engineering Students

PREREQUISITES :

Prior knowledge in Science/ Engineering/ Maths/ Statistics is required.

OVERVIEW :

Data Science careers are one of the most highly compensated careers worldwide currently. Due to its broad application in every industry, there is a higher demand for data scientists who can analyze complex data and communicate the results effectively. In this course, we will be covering the basics of data science and learning the tools used for data science from scratch. We will start the lessons at the beginner level and move up to the advanced level.

COURSE COVERAGE :

LANGUAGE SKILLS

PYTHON Basic syntax and data types - Control flow - functions and modules - File handling - Exception handling - List comprehensions and lambda functions - Object-oriented programming(OOP) concept.

DATA MANAGEMENT

MySQL Creating and modifying tables - Data manipulation (INSERT, UPDATE, DELETE) - Querying databases (SELECT, JOIN, GROUP BY)- Aggregation fuctions (SUM, AVG, COUNT) JOIN AND Subquerie.

MongoDB Introduction to NoSQL databases - CRUD operations - Data Indexing & Aggregation - Working with Unstructured Data

DATA WRANGLING

NUMPY Arrays and array operations - Indexing and slicing - Mathematical functions - Linear algebra operations - Random module - Broadcasting.

PANDAS Series and Data Frame - Indexing and selecting data - Data cleaning and manipulation - Merging and joining data - Grouping and aggregation data - Handlinng missing data - Time series data.

DATA VISUALIZATION

MATPLOTLIB Basic ploting (line plots, bar plots)- Customizing plots (labels, titles, legends) - Multiple subplots - Histograms and box plots - 3D plotting.

MACHINE LEARNING

SCIKIT-LEARN Data preprocessing - Model selection and evaluation - Supervised learning algorithms (e.g., clustering, dimensionally reduction) - pipelines.

DEEP LEARNING

TENSORFLOW & KERAS Basics of neural networks -CNN - RNN - NLP - Building and training simple models - layers and activiation functions - Loss functions and optimizers - Model evaluation and validation - Transfer learning.

CODE MANAGEMENT

GIT Setting up a GIT repository - Version Control - Branching & Merging - Collaboration.