Data Science in Python – Data Science Academy

About Course

Your first step toward a career in Data Analysis and AI.

Course Overview

Duration: 62 hours — 10 to 12 weeks at 5–6 hours/week
Level: Beginner to Advanced
Prerequisites: Basic computer literacy — no Python, SQL, or coding experience needed
Recommended Prior Courses: Excel for Data Analyst or Power BI for Data Analyst (datasciencehub.cloud)
Target Audience: Data analysts, business analysts, Excel power users, Power BI developers, and anyone who works with data and wants to automate and scale their analysis
Tools: Python 3.11+, Anaconda, Jupyter Notebook, VS Code, Pandas, NumPy, Matplotlib, Seaborn, Plotly, Plotly Dash, SQLite, PostgreSQL, openpyxl, xlsxwriter

Course Objectives

By the end of this course, students will be able to:

Set up a professional Python environment and write clean, readable code from day one
Manipulate and transform any dataset using Pandas with full confidence
Clean real-world messy data — handle missing values, duplicates, outliers, and inconsistent formats
Create publication-quality static and interactive charts using Matplotlib, Seaborn, and Plotly
Build interactive multi-chart dashboards using Plotly Dash
Apply statistical analysis — descriptive stats, hypothesis testing, correlation, regression, and A/B testing
Connect Python to databases (SQLite, PostgreSQL, MySQL) and query data directly into DataFrames
Pull live data from REST APIs and extract tables from the web using Python
Automate Excel reports, generate PDFs, and schedule scripts to run without manual intervention
Build a complete end-to-end data analysis project and publish it as a GitHub portfolio piece

Next Steps After This Course

SQL for Data Analysts (datasciencehub.cloud) — pair Python with SQL for complete full-stack analytics workflows
Data Visualization Mastery with Power BI (datasciencehub.cloud) — combine Python-powered data prep with Power BI storytelling
Machine Learning for Analysts (datasciencehub.cloud) — extend your Python skills into predictive analytics and ML models
Google BigQuery & Cloud Analytics — scale your Python and SQL skills to cloud-based data warehouses
Analytics Engineering — dbt, data pipelines, and dimensional modeling for production analytics

Master Python fundamentals — data types, functions, loops, and clean code practices
Manipulate and transform any dataset using Pandas and NumPy
Clean real-world messy data — missing values, duplicates, outliers, and inconsistent formats
Visualize data with Matplotlib, Seaborn, and Plotly — static and interactive charts
Build interactive multi-chart dashboards using Plotly Dash
Apply statistical analysis — descriptive stats, hypothesis testing, A/B testing, and forecasting
Connect Python to databases and run SQL queries directly into DataFrames
Pull live data from REST APIs and scrape structured tables from websites
Automate Excel reports, generate PDFs, and schedule scripts without manual effort
Build a complete end-to-end analysis project and publish it as a GitHub portfolio piece

Course Content

Module 01 — Python Foundations for Analysts (6 Hours)

Introduction
1.1 Environment Setup — Anaconda, Jupyter, JupyterLab, VS Code
1.2 Python Basics & Syntax — variables, naming, indentation, PEP 8
1.3 Data Types Deep Dive — int, float, str, bool, NoneType with analyst use cases
1.4 Operators & Expressions — arithmetic, comparison, logical operators
1.5 Strings for Data Work — string methods, slicing, f-strings, number formatting
1.6 Working with Dates & Times — datetime module, parsing, arithmetic, strftime
1.7 Input, Output & File Paths — print formatting, reading .txt/.csv, pathlib
1.8 Error Handling Basics — try/except, FileNotFoundError, ValueError, KeyError
1.9 Module Project: Personal Sales Calculator

Module 02 — Data Structures & Control Flow (5 Hours)

2.1 Lists — The Analyst’s Workhorse — indexing, slicing, sorting, filtering, nested lists
2.2 Tuples & Sets — immutable data, set operations (union/intersection/difference)
2.3 Dictionaries — creating, accessing, updating, dict comprehensions, nesting
2.4 Conditional Logic — if/elif/else, nested conditions, ternary expressions
2.5 Loops for Data Processing — for, while, enumerate, zip, range
2.6 List Comprehensions & Generators — filtering, mapping, transforming data
2.7 Functions — def, parameters, return, *args/**kwargs, docstrings, lambda
2.8 Module Project: Student Grade Analyzer

Module 03 — NumPy — Numerical Computing (4 Hours)

Module 04 — Pandas — Data Manipulation Mastery (8 Hours)

4.1 DataFrames & Series Fundamentals — index, dtypes, shape, info(), describe()
4.2 Loading Data from Any Source — read_csv, read_excel, read_json, read_sql, key parameters
4.3 Selecting & Filtering Data — loc vs iloc, boolean indexing, query(), multi-conditions
4.4 Sorting, Ranking & Reindexing — sort_values, sort_index, rank(), nlargest, reset_index
4.5 Adding & Transforming Columns — assign(), apply(), map(), str accessor, vectorized ops
4.6 GroupBy — The Analyst’s Super Power — groupby(), agg(), transform(), named aggregations
4.7 Merging & Joining DataFrames — merge(), join(), concat(), all join types, duplicate keys
4.8 Pivot Tables & Crosstabs — pivot_table(), pd.crosstab(), margins, multi-level pivots
4.9 Time Series with Pandas — DatetimeIndex, resample(), rolling(), shift(), date offsets
4.10 Exporting Data — to_csv, to_excel, ExcelWriter for multi-sheet workbooks
4.11 Module Project: E-Commerce Sales Deep Dive

Module 05 — Data Cleaning & Wrangling (7 Hours)

5.1 Data Quality Assessment — profiling, missing values, duplicates, value distributions
5.2 Handling Missing Data — dropna, fillna, interpolate, forward/backward fill; when to impute vs drop
5.3 Dealing with Duplicates — duplicated(), drop_duplicates(), near-duplicate detection
5.4 Data Type Corrections — astype(), pd.to_numeric, pd.to_datetime, errors=’coerce’
5.5 String Cleaning & Regex — str.strip/replace/extract/contains, regex for phone/email/address
5.6 Outlier Detection & Treatment — IQR method, z-score, capping, flooring, visual detection
5.7 Reshaping: Melt, Stack, Explode — melt(), stack(), unstack(), explode(); wide vs long format
5.8 Building a Reusable Cleaning Pipeline — modular functions, .pipe() chaining, cleaning templates
5.9 Module Project: Hospital Patient Records Cleanup

Module 06 — Data Visualization with Python (7 Hours)

6.1 Chart Selection Framework — comparison, distribution, relationship, composition, trends
6.2 Matplotlib Foundations — Figure/Axes anatomy, subplots, line/bar/scatter/pie, saving images
6.3 Seaborn — Statistical Visualization — distplot, boxplot, violinplot, heatmap, pairplot, FacetGrid
6.4 Advanced Chart Types — dual-axis, area, bubble, waterfall, Gantt-style charts
6.5 Design Principles for Analysts — color palettes, chartjunk, annotations, titles, chart polish
6.6 Plotly — Interactive Charts — plotly.express, go.Figure, hover data, color encoding, HTML export
6.7 Interactive Dashboards with Plotly Dash — layout, callbacks, dropdowns, sliders, multi-chart apps
6.8 Visualizing Time Series — trend lines, seasonal decomposition, candlesticks, event annotations
6.9 Chart Automation & Reporting — generating charts in loops, embedding in PDF/Word, email delivery
6.10 Module Project: Interactive Sales Dashboard

Module 07 — Statistical Analysis for Analysts (6 Hours)

7.1 Descriptive Statistics Mastery — mean, median, mode, variance, std, skewness, kurtosis — interpreting for business
7.2 Probability Distributions — normal, binomial, Poisson; scipy.stats; PDF/CDF/PPF
7.3 Sampling & Confidence Intervals — CLT, random sampling strategies, margin of error
7.4 Hypothesis Testing — p-values, t-tests, chi-square; interpreting results for non-technical stakeholders
7.5 Correlation Analysis — Pearson, Spearman, Kendall; matrices, heatmaps, multicollinearity
7.6 Linear Regression for Analysts — OLS with statsmodels, R², residuals, prediction (not ML — pure analytics)
7.7 A/B Testing with Python — test design, sample size calculation, t-test for proportions, interpreting lift
7.8 Business Forecasting Basics — moving averages, exponential smoothing, trend + seasonality decomposition
7.9 Module Project: Marketing Campaign A/B Test Analysis

Module 08 — SQL with Python & Database Access (5 Hours)

8.1 SQLite with Python (sqlite3) — connecting, executing queries, fetchall, results into DataFrames
8.2 PostgreSQL & MySQL Connections — psycopg2, mysql-connector, secure credential handling
8.3 SQLAlchemy — The Professional Way — engine, connection, pd.read_sql(), to_sql()
8.4 Complex SQL in Python — multi-table JOINs, subqueries, CTEs, window functions via Python
8.5 pandasql — SQL on DataFrames — writing SQL syntax against Pandas DataFrames
8.6 REST API Data Extraction — requests library, JSON parsing, pagination, authentication
8.7 Web Scraping Basics — BeautifulSoup, ethical scraping, pd.read_html() for table extraction
8.8 Module Project: Retail Database Analytics System

Module 09 — Automation & Reporting (6 Hours)

9.1 Excel Automation with openpyxl — workbooks, formatting, charts, conditional formatting, sheet protection
9.2 Advanced Excel with xlsxwriter — charts, sparklines, data validation, professional formatting
9.3 Automating Excel with xlwings — interact with open files, run macros, fill templates
9.4 PDF Report Generation — reportlab/fpdf2 for programmatic PDFs, embedding charts, logos, KPI tables
9.5 Email Automation — smtplib, MIME, HTML emails with attachments, automated weekly delivery
9.6 Task Scheduling — schedule library, Windows Task Scheduler, cron jobs; running scripts automatically
9.7 Building a Simple ETL Pipeline — Extract-Transform-Load pattern, logging, error handling, retry logic
9.8 Python Scripts vs Notebooks — when to use .py files, argparse CLI tools, project structure best practices
9.9 Module Project: Automated Weekly Business Report

Module 10 — Capstone: End-to-End Analysis Project (8 Hours)

10.1 Project Scoping & Business Questions — defining 5 analytical questions, stakeholder framing, success metrics
10.2 Data Collection & Cleaning — full cleaning pipeline, data quality audit documentation
10.3 Exploratory Data Analysis — univariate/bivariate analysis, distributions, outliers, correlation matrix
10.4 Advanced Analysis — segmentation, trend analysis, statistical tests, regression for forecasting
10.5 Visualization & Dashboard — complete chart suite answering all 5 business questions + interactive dashboard
10.6 Automated Reporting — Excel workbook + PDF summary generated automatically from final dataset
10.7 Presentation & Portfolio — project presentation structure, GitHub README, showcasing to employers
10.8 Capstone Project: Full Business Analysis Portfolio Piece

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About Course

Your first step toward a career in Data Analysis and AI.

What Will You Learn?

Course Content

Module 01 — Python Foundations for Analysts (6 Hours)

Introduction

1.1 Environment Setup — Anaconda, Jupyter, JupyterLab, VS Code

1.2 Python Basics & Syntax — variables, naming, indentation, PEP 8

1.3 Data Types Deep Dive — int, float, str, bool, NoneType with analyst use cases

1.4 Operators & Expressions — arithmetic, comparison, logical operators

1.5 Strings for Data Work — string methods, slicing, f-strings, number formatting

1.6 Working with Dates & Times — datetime module, parsing, arithmetic, strftime

1.7 Input, Output & File Paths — print formatting, reading .txt/.csv, pathlib

1.8 Error Handling Basics — try/except, FileNotFoundError, ValueError, KeyError

1.9 Module Project: Personal Sales Calculator

Module 02 — Data Structures & Control Flow (5 Hours)

2.1 Lists — The Analyst’s Workhorse — indexing, slicing, sorting, filtering, nested lists

2.2 Tuples & Sets — immutable data, set operations (union/intersection/difference)

2.3 Dictionaries — creating, accessing, updating, dict comprehensions, nesting

2.4 Conditional Logic — if/elif/else, nested conditions, ternary expressions

2.5 Loops for Data Processing — for, while, enumerate, zip, range

2.6 List Comprehensions & Generators — filtering, mapping, transforming data

2.7 Functions — def, parameters, return, *args/**kwargs, docstrings, lambda

2.8 Module Project: Student Grade Analyzer

Module 03 — NumPy — Numerical Computing (4 Hours)

3.1 Arrays vs Lists — Why NumPy? — dtype, shape, ndim, broadcasting intro

3.2 Array Operations & Vectorization — element-wise ops, performance vs loops

3.3 Indexing, Slicing & Filtering — 1D/2D indexing, boolean masking, fancy indexing

3.4 Statistical Functions — mean, median, std, var, percentile, cumsum, corrcoef, axis

3.5 Reshaping & Stacking — reshape, transpose, flatten, concatenate, vstack, hstack

3.6 Random & Simulation — np.random, synthetic datasets, reproducibility with seeds

3.7 Module Project: Sales Performance Benchmarking

Module 04 — Pandas — Data Manipulation Mastery (8 Hours)

4.1 DataFrames & Series Fundamentals — index, dtypes, shape, info(), describe()

4.2 Loading Data from Any Source — read_csv, read_excel, read_json, read_sql, key parameters

4.3 Selecting & Filtering Data — loc vs iloc, boolean indexing, query(), multi-conditions

4.4 Sorting, Ranking & Reindexing — sort_values, sort_index, rank(), nlargest, reset_index

4.5 Adding & Transforming Columns — assign(), apply(), map(), str accessor, vectorized ops

4.6 GroupBy — The Analyst’s Super Power — groupby(), agg(), transform(), named aggregations

4.7 Merging & Joining DataFrames — merge(), join(), concat(), all join types, duplicate keys

4.8 Pivot Tables & Crosstabs — pivot_table(), pd.crosstab(), margins, multi-level pivots

4.9 Time Series with Pandas — DatetimeIndex, resample(), rolling(), shift(), date offsets

4.10 Exporting Data — to_csv, to_excel, ExcelWriter for multi-sheet workbooks

4.11 Module Project: E-Commerce Sales Deep Dive

Module 05 — Data Cleaning & Wrangling (7 Hours)

5.1 Data Quality Assessment — profiling, missing values, duplicates, value distributions

5.2 Handling Missing Data — dropna, fillna, interpolate, forward/backward fill; when to impute vs drop

5.3 Dealing with Duplicates — duplicated(), drop_duplicates(), near-duplicate detection

5.4 Data Type Corrections — astype(), pd.to_numeric, pd.to_datetime, errors=’coerce’

5.5 String Cleaning & Regex — str.strip/replace/extract/contains, regex for phone/email/address

5.6 Outlier Detection & Treatment — IQR method, z-score, capping, flooring, visual detection

5.7 Reshaping: Melt, Stack, Explode — melt(), stack(), unstack(), explode(); wide vs long format

5.8 Building a Reusable Cleaning Pipeline — modular functions, .pipe() chaining, cleaning templates

5.9 Module Project: Hospital Patient Records Cleanup

Module 06 — Data Visualization with Python (7 Hours)

6.1 Chart Selection Framework — comparison, distribution, relationship, composition, trends

6.2 Matplotlib Foundations — Figure/Axes anatomy, subplots, line/bar/scatter/pie, saving images

6.3 Seaborn — Statistical Visualization — distplot, boxplot, violinplot, heatmap, pairplot, FacetGrid

6.4 Advanced Chart Types — dual-axis, area, bubble, waterfall, Gantt-style charts

6.5 Design Principles for Analysts — color palettes, chartjunk, annotations, titles, chart polish

6.6 Plotly — Interactive Charts — plotly.express, go.Figure, hover data, color encoding, HTML export

6.7 Interactive Dashboards with Plotly Dash — layout, callbacks, dropdowns, sliders, multi-chart apps

6.8 Visualizing Time Series — trend lines, seasonal decomposition, candlesticks, event annotations

6.9 Chart Automation & Reporting — generating charts in loops, embedding in PDF/Word, email delivery

6.10 Module Project: Interactive Sales Dashboard

Module 07 — Statistical Analysis for Analysts (6 Hours)

7.1 Descriptive Statistics Mastery — mean, median, mode, variance, std, skewness, kurtosis — interpreting for business

7.2 Probability Distributions — normal, binomial, Poisson; scipy.stats; PDF/CDF/PPF

7.3 Sampling & Confidence Intervals — CLT, random sampling strategies, margin of error

7.4 Hypothesis Testing — p-values, t-tests, chi-square; interpreting results for non-technical stakeholders

7.5 Correlation Analysis — Pearson, Spearman, Kendall; matrices, heatmaps, multicollinearity

7.6 Linear Regression for Analysts — OLS with statsmodels, R², residuals, prediction (not ML — pure analytics)

7.7 A/B Testing with Python — test design, sample size calculation, t-test for proportions, interpreting lift

7.8 Business Forecasting Basics — moving averages, exponential smoothing, trend + seasonality decomposition

7.9 Module Project: Marketing Campaign A/B Test Analysis

Module 08 — SQL with Python & Database Access (5 Hours)

8.1 SQLite with Python (sqlite3) — connecting, executing queries, fetchall, results into DataFrames

8.2 PostgreSQL & MySQL Connections — psycopg2, mysql-connector, secure credential handling

8.3 SQLAlchemy — The Professional Way — engine, connection, pd.read_sql(), to_sql()

8.4 Complex SQL in Python — multi-table JOINs, subqueries, CTEs, window functions via Python