Mastering Data Analytics: A Comprehensive Program
This program provides a comprehensive journey into the world of data analytics, covering essential programming skills, statistical foundations, machine learning techniques, and powerful data visualization tools. You'll gain hands-on experience with industry-leading libraries and platforms, preparing you for a successful career in data science.
Python for Data Analytics: Fundamentals
Python Basics
- Variables: Declaration, naming, type inference
- Data Types: int, float, str, list, tuple, dict, set
- Loops: for, while, nested loops
- Functions: def, parameters, return, lambda
Advanced OOP Concepts
- Classes and Objects: Creation
- Inheritance: Single, multiple, multilevel
- Encapsulation: Private attributes, getter/setter
- Polymorphism: Method overriding/overloading
File & Exception Handling
- Working with Files: Reading, writing, appending (.txt, .csv)
- Exception Handling: try, except, finally, custom exceptions
Key Python Libraries for Data Analytics
NumPy – Numerical Computing
Arrays: Creation, indexing, slicing. Broadcasting: Operations on arrays of different shapes. Mathematical Operations: Vectorized computations, stats functions.
Pandas – Data Manipulation
DataFrames & Series: Creation, indexing, selection. Data Cleaning: Handling NaNs, replacing, dropping. GroupBy: Aggregation and transformation. Merging/Joining: concat, merge, join.
Matplotlib & Seaborn – Visualization
Matplotlib: Line, bar, pie, subplots. Seaborn: Heatmap, pairplot, boxplot, violinplot, correlation plots.
Streamlit – Interactive Dashboards
Basics: st.write(), widgets, layout. Data Display: Charts, tables, metrics. User Inputs: sliders, checkboxes. Deployment: Sharing apps online.
Statistics for Data Science: Core Concepts
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Descriptive Statistics
Measures of Central Tendency (Mean, Median, Mode), Dispersion (Range, Variance, Standard Deviation), and Shape (Skewness, Kurtosis). Visualizations include Histograms and Boxplots.
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Probability & Distributions
Basic Probability (Events, Sample Space, Conditional Probability) and key distributions like Normal, Binomial, and Poisson. Understanding the Central Limit Theorem.
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Hypothesis Testing
Formulating Null & Alternative Hypotheses. Statistical Tests: Z-test, t-test, Chi-square test. Interpreting p-values, significance levels, and confidence intervals.
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Correlation vs. Causation
Understanding Correlation Coefficient (Pearson, Spearman) and Scatterplots. Exploring Causality Concepts like Simpson’s paradox and confounding variables with examples.
Multivariate Analysis and Data Preprocessing
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Univariate Analysis
Analysis of a single variable using histograms, boxplots, value counts, and summary statistics. Useful for understanding age distribution or income levels.
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Bivariate Analysis
Examining relationships between two variables with scatterplots, correlation matrices, and cross-tabulation. Examples include salary vs. experience.
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Multivariate Analysis
Analyzing more than two variables using pairplots, heatmaps, grouped boxplots, and introductory PCA. Applied to sales performance across multiple dimensions.
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Data Preprocessing & Cleaning
Techniques include handling missing data, encoding (Label, One-hot), scaling & normalization, feature engineering (Binning, DateTime), and managing duplicates/outliers.
Machine Learning for Data Analytics
ML in Analytics
Applying ML algorithms to extract insights, predict outcomes, and support business decisions, moving from descriptive to predictive analytics.
ML Workflow
A step-by-step pipeline: Data Collection → Cleaning → Feature Engineering → Model Selection → Evaluation → Deployment, with Scikit-Learn integration.
Supervised Learning
Includes Linear Regression, Logistic Regression, Decision Trees, Random Forest, and SVM for predictive modeling.
Unsupervised Learning
Techniques like K-Means and Hierarchical Clustering for pattern discovery and segmentation.
Model Evaluation
Metrics such as Accuracy, Precision, Recall, ROC-AUC. Utilizing Train-Test Split, Cross-validation, and Hyperparameter Tuning.
SQL for Data Analysis: Essential Queries
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Basic SQL Queries
SELECT to retrieve columns, WHERE to filter rows based on conditions, and ORDER BY to sort results in ascending or descending order.
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Aggregation and Clauses
GROUP BY to group rows and apply aggregate functions like COUNT(), SUM(), AVG(), MAX(), MIN(). HAVING filters grouped data.
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SQL JOINS
INNER JOIN for matching rows, LEFT JOIN for all left table rows, RIGHT JOIN for all right table rows, and FULL JOIN for all rows from both tables.
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Subqueries & CTEs
Subqueries are queries inside a main query. CTEs (Common Table Expressions) are temporary named result sets using WITH clause to simplify complex queries.
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Window Functions
RANK() assigns rank with gaps for duplicates, while ROW_NUMBER() assigns a unique sequential number to rows.
Power BI: Building Interactive Dashboards
Introduction to Power BI
Understanding Power BI Desktop vs. Service, interface overview, and the ecosystem of Reports, Dashboards, Datasets, and Workspaces.
Connecting & Transforming Data
Importing data from Excel, CSV, SQL, Web APIs. Data transformation in Power Query Editor: cleaning, merging, custom columns with M language.
Data Modeling & Visualizations
Establishing relationships (One-to-Many), understanding Star/Snowflake Schema, and creating various charts like Bar, Line, Pie, and Maps.
Filters & DAX Fundamentals
Implementing visual, page, and report-level filters, slicers, drill-down. Introduction to DAX for calculated columns and measures (SUM(), AVERAGE(), CALCULATE()).
Big Data Technologies: ETL, Hadoop, PySpark
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PySpark for Big Data
PySpark Basics: RDDs vs DataFrames. Transformations and Actions. Spark SQL. Handling Large Datasets, DataFrame Joins, Aggregations.
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Big Data & Hadoop
Big Data Architecture & 3 Vs (Volume, Velocity, Variety). HDFS (Hadoop Distributed File System). Hadoop Ecosystem: Hive, Pig, Sqoop, HBase. Intro to YARN and MapReduce.
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ETL Concepts
ETL Lifecycle and Tools (Talend/NiFi/Airflow). Batch vs Streaming ETL. Data Cleaning, Mapping, Scheduling. Building Data Pipelines using Python.
Cloud Data Warehousing & Data Ethics
Snowflake: Cloud Data Warehouse
Introduction to Cloud Warehousing & Architecture. Snowflake Tables, Schemas, Stages. Loading Data from CSV/S3 into Snowflake. Querying with SQL. Virtual Warehouses, Cost Optimization.
Data Ethics & Governance
Understanding Data Privacy Laws: GDPR, CCPA. Addressing Data Bias & Fairness. Concepts of Consent, Ownership, and Usage Ethics. Principles of Responsible AI.
