Data Science With Gen AI

Internet is growing exponentially and so are businesses. Doing business in the era of digitisation is easy but making its strategy, analysing the data, forecasting the future, comparative analysis with the competitors' reports is not a layman's task. So why spend crores of rupees and sleepless nights evaluating all.

Use Data Science get the work done at a lesser cost and smaller duration. From fraud detection to weather forecasting, company's decision making to forecasting the future, everything is Data Science' game.

This is a combination of maths and programming, advanced analytics, Artificial Intelligence and Machine Learning which makes the calculations and forecasts readily available for the end users.
Learn Big Data, AI and ML under one umbrella i.e Data Science and become a Data Scientist.

Syllabus:

• Python & Libraries
• Python(Core + Advance)
• Variables
• Data types
• Operators
• if statements
• Loops
• String
• Lists, Tuples
• Dictionaries
• Sets
• Conditional expressions
• Modules
• Functions
• File handling
• Object Oriented Concept
• Exception Handling
• GUI Progragramming in Python
• 5 Mini Projects
• Pandas
• NumPy
• Data Visualization
• Excel
• Basic Formulas: SUM, AVERAGE, MEAN, MEDIAN, SUMPRODUCT, CONCATENATE
• Advance Formulas: VLOOKUP, INDEX, MATCH, IF, COUNTIF, SUMIF, Remove duplicates and conditional formatting
• Charts & Filters: Charts, filters, sort and slicers, Pivot tables and pivot charts
• Matplotlib
• Seaborn
• Powr BI
• Maths & Statistics
• Basic Math
• Arithmetic
• Weighted average
• Cumulative sum
• Percentile
• Linear Algebra
• Calculus
• Probability
• Statistics
• Database
• MySQL/SQL
• Introduction to MySQL
• Basic MySQL syntax
• Schema Design Methodology
• Create / Select / Drop / Copy of Database
• Create Table
• Constraints in MySQL
• Alter Table
• Insert / Select / Delete / Update Records
• Rollback, Commit and Grant
• Machine Learning
• Preprocessing
• Handling NA values
• Outlier treatment
• Data normalization
• One hot encoding
• Label encoding
• Feature engineering
• Train test split
• Cross validation
• Model Building
• Deep Learning
• Pytorch / Tensorflow
• What is a neural network?
• Forward propagation
• Back propagation
• Building multilayer perceptron
• Special neural network architectures: Convolutional neural network (CNN)
• Sequence models: RNN, LSTM
• Natural Language Processing (NLP)
• NLP
• Regex
• Text presentation: Count vectorizer, TF-IDF, BOW, Word2Vec, Embeddings
• Text classification: Naïve Bayes
• Fundamentals of Spacy & NLTP library
• One end to end project
• Control Version
• Git & GitHub
• Intro to VCS
• Types of VCS
• What is Git, GitHub
• Git Installation
• Configuring Git
• Connecting Git with GitHub
• Basic Git & GitHub Workflow
• Git Branching & Merging
• Git Clone
• Resolve Merge Conflict
• How to setup & upload Project on GitHub
• How to PUSH & Pull Project