Data Analytics 

1. Foundations of Data Analytics

Topics:

• Introduction to data analytics: Goals, types (descriptive, diagnostic, predictive, prescriptive).

• Data types: Structured (SQL) vs. unstructured (text, images).

• Data lifecycle: Collection, cleaning, analysis, visualization, decision-making.

• Ethics & privacy: GDPR, bias, and responsible data use.

Tools:

• Python, Jupyter Notebooks, Excel, Google Sheets.

2. Data Wrangling & Preprocessing

Topics:

• Data cleaning: Handling missing values, outliers, duplicates.

• Data transformation: Normalization, scaling, encoding (one-hot, label).

• Feature engineering: Creating new variables for analysis.

• APIs & web scraping (introductory).

Tools:

• Python (Pandas, NumPy), OpenRefine, SQL.

3. Exploratory Data Analysis (EDA) & Visualization

Topics:

• Statistical summaries: Mean, median, distributions, correlations.

• Visualization techniques: Histograms, scatter plots, box plots, heatmaps.

• Storytelling with data: Dashboards and reports.

• Geospatial and time-series data visualization.

Tools:

• Matplotlib, Seaborn, Tableau, Power BI, Plotly.

4. Statistical Analysis & Machine Learning Basics

Topics:

• Hypothesis testing: p-values, t-tests, chi-square.

• Regression analysis: Linear, logistic, polynomial.

• Classification: Decision trees, k-NN, SVM.

• Clustering: k-means, hierarchical.

• Model evaluation: Accuracy, precision, recall, ROC curves.

Tools:

• Scikit-learn, StatsModels, R (optional).

5. Advanced Topics & Big Data

Topics:

• Big Data tools: Hadoop, Spark, SQL vs. NoSQL (MongoDB).

• Cloud platforms: AWS (Redshift, S3), Google BigQuery.

• AI integration: NLP basics (sentiment analysis), deep learning intro.

• Real-time analytics: Streaming data (Kafka, Spark Streaming).

Tools:

• Apache Spark, SQL, AWS/GCP, TensorFlow (intro).

6. Capstone Project & Deployment

Topics:

• End-to-end project: Solve a real-world problem (e.g., sales forecasting, customer segmentation).

• Model deployment: Flask/Django APIs, Tableau dashboards.

• Presenting insights: Technical reports and stakeholder presentations.

Tools:

• Full-stack: Python + SQL + Tableau + Cloud.