Data Science And Big Data Analytics Syllabus

• Overview of Data Science and its evolution
• Importance and scope of data-driven decision-making
• Roles: Data Scientist, Analyst, Engineer – job roles & responsibilities
• Data Science vs. Big Data vs. Business Intelligence
• Data Science lifecycle (CRISP-DM)

Python Programming

• Python basics: Variables, Data Types, Operators
• Control statements: Loops and Conditions
• Functions, Modules, and File Handling
• Object-Oriented Programming concepts

Python for Data Analysis

• NumPy: Arrays, operations, and indexing
• Pandas: Series, DataFrames, importing/exporting data, grouping
• Matplotlib & Seaborn: Data visualization and customization

R Programming (Optional)

• R Basics: Data structures, functions, packages
• Tidyverse: ggplot2, dplyr, tidyr
• Data import/export and visualization in R

• Types of data and data scales
• Descriptive statistics: Mean, Median, Mode, Standard Deviation
• Inferential statistics: Sampling, Central Limit Theorem
• Probability concepts: Bayes Theorem, probability distributions
• Hypothesis testing: T-test, Chi-square, ANOVA
• Correlation vs Causation, Linear Regression fundamentals

• Handling missing data, outliers, duplicates
• Encoding categorical variables
• Feature scaling: Standardization, Normalization
• Feature engineering basics
• Data transformation techniques

• Visual storytelling with data
• Static and dynamic charts with Python (Matplotlib, Seaborn)
• Dashboard creation with Power BI/Tableau
• Heatmaps, pairplots, histograms, bar plots, box plots
• Using visuals for decision-making and reporting

Supervised Learning

• Linear & Logistic Regression
• Decision Trees, Random Forest, Gradient Boosting
• Support Vector Machines (SVM)
• Model performance metrics: Accuracy, Precision, Recall, F1, ROC-AUC

Unsupervised Learning

• K-Means and Hierarchical Clustering
• Dimensionality reduction with PCA
• Association Rule Mining

Model Tuning and Evaluation

• Cross-validation techniques
• Bias-variance tradeoff
• Hyperparameter tuning (GridSearchCV, RandomizedSearchCV)

• Introduction to Big Data: Volume, Velocity, Variety
• Hadoop Ecosystem overview: HDFS, MapReduce, YARN
• Working with data in Hadoop
• Hive for SQL-like querying
• Pig for data transformation
• HBase basics

• Spark Core architecture
• RDDs and DataFrames
• SparkSQL for querying
• Spark Streaming for real-time processing
• Integration with HDFS and Hive

• Basics of ETL: Extract, Transform, Load
• ETL tools overview: Talend, Apache NiFi, Airflow
• Building and automating data pipelines
• Working with structured vs unstructured data
• Data warehousing concepts: OLAP vs OLTP, Snowflake, Redshift

• Introduction to cloud providers: AWS, Azure, Google Cloud
• Cloud services for data storage & compute: S3, EC2, BigQuery, Azure Blob
• Hosting Jupyter Notebooks on the cloud
• ML model deployment with cloud tools
• Big Data services: Amazon EMR, Google DataProc, Azure HDInsight

• Complete end-to-end Data Science Project
• Business case problem-solving with real-time datasets
• Data analysis, model building, and deployment
• Presentation and storytelling with data
• Git/GitHub for version control and collaboration