• What is Data Science
• History of Data Science
• Data Science Process and Lifecycle
• Data Science Roles: Data Scientist, Data Engineer, Data Analyst
• Data Science Applications in Various Industries

Python Programming
• Basics: Syntax, Variables, Data Types
• Loops and Conditional Statements
• Functions and Modules
• Data Structures: Lists, Tuples, Dictionaries, Sets
• Libraries: NumPy, Pandas, Matplotlib, Seaborn
R Programming
• Basics: Syntax, Variables, Data Types
• dplyr for data manipulation
• ggplot2 for data visualization
• R for statistical analysis

•Importing and Exporting Data
• Cleaning Data: Handling Missing Values, Outliers
• Data Transformation: Sorting, Filtering, Aggregation
• Merging and Joining Data Sets
Data Visualization
• Basic Plots: Line, Bar, Histogram, Pie
• Advanced Visualizations: Boxplot, Heatmap, Pair Plot
• Interactive Visualizations with Polty
• Dashboard Creation with Tableau or Power BI

• Descriptive Statistics
• Measures of Central Tendency: Mean, Median, Mode
• Measures of Dispersion: Range, Variance, Standard Deviation
• Skewness and Kurtosis
• Inferential Statistics
• Probability Theory and Distributions
• Sampling and Sampling Distributions
• Hypothesis Testing: t-tests, Chi-Square Test, ANOVA
• Confidence Intervals and p-Values
  Correlation and Causation

Supervised Learning
• Regression Analysis: Linear, Logistic
• Classification Algorithms: Decision Trees, K-Nearest Neighbors, Support Vector Machines, Na-ive Bayes
• Model Evaluation: Confusion Matrix, ROC Curve, as well as Precision, Recall, and F1 Score
Unsupervised Learning
• Clustering: K-Means, Agglomerative Clustering, DBSCAN
• Dimensionality Reduction: PCA, t-SNE

Model Optimization
• Cross-Validation Techniques
• Hyperparameter Tuning: Grid Search, Random Search
• Ensemble Methods: Bagging, Boosting, Random Forest and Gradient Boosting Machines (GBM), XGBoost

Supervised Learning
• Regression Analysis: Linear, Logistic
• Classification Algorithms: Decision Trees, K-Nearest Neighbors, Support Vector Machines, Na-ive Bayes
• Model Evaluation: Confusion Matrix, ROC Curve, as well as Precision, Recall, and F1 Score
Unsupervised Learning
• Clustering: K-Means, Agglomerative Clustering, DBSCAN
• Dimensionality Reduction: PCA, t-SNE

Deep Learning
• An Introduction to Neural Networks
• Deep Learning Frameworks: TensorFlow, Keras, PyTorch
Introduction of Convolutional Neural Networks (CNNs) for image processing
• Application of Recurrent Neural Networks (RNNs) in time series and NLP
• Transfer Learning and Pre-trained Models
Deep Learning Techniques
• PREPROCESSING OF TEXT: TOKENIZATION, LEMMATIZATION, STOP WORDS REMOVAL
SENTIMENT ANALYSIS, TOPIC MODELING
• You will learn through word embeddings (Word2Vec, Glove) and sequence models (LSTM, GRU), which include LSTM and GRU.
Time Series Analysis

• Decomposition of Time Series
• ARIMA and Exponential Smoothing
• Forecasting as well as Anomaly Detection

Hadoop Ecosystem

• Introduction of Hadoop and HDFS
• The MapReduce Programming Model
• Data Processing with Apache Pig and Hive

Apache Spark

• Spark Core and RDDs

• Spark SQL and Data Frames

• Spark Streaming

• Machine Learning with MLlib

NoSQL Databases

• The beginning of NoSQL

• NoSQL databases like MongoDB and Cassandra

• Key-value stores and Document Databases

The ETL Processes
– Data Ingestion: Batch as well as Real-time
– Data Cleaning and Transformation
– Data Loading Techniques

Data Pipeline Design
• Pipeline orchestration using Apache Airflow
• Data integration tools: Talend, Informatica

Cloud Data Engineering
• AWS Data Services: S3, Redshift, Glue
• About Google Cloud Data Services like Big Query, Dataflow, Dataproc
• Azure like Data Lake, Synapse Analytics

Jupyter Notebooks
• Setup and Use of Jupyter Notebooks
• Interactive Data Science Workflows
*Control of Version
• Git and GitHub for Collaborative Work
• Code Repositories Management
Containerization
• Docker Introduction
• Creating and Managing Docker Containers

Cloud platforms
• AWS, Azure, Google Cloud
• < UNK> This part of the article mainly focuses on deploying data science projects on cloud platforms.

Career Choice
• You mean a world crisis
• Build scope and objectives
Data Collection and Preprocessing
• Collecting data from different sources
• Data cleaning and preparation
Model construction and analysis
• Machine learning algorithms
• Model tuning and optimization
Description of results
• Assessment of findings
• Comprehensive reporting
• Instruction to the users

Career Choice
• You mean a world crisis
• Build scope and objectives
Data Collection and Preprocessing
• Collecting data from different sources
• Data cleaning and preparation
Model construction and analysis
• Machine learning algorithms
• Model tuning and optimization
Description of results
• Assessment of findings
• Comprehensive reporting
• Instruction to the users