Python Course for Data Analysis - Become Data Analyst (2026)

Learn to set up and use Google Colab for data analysis, including notebooks, coding and markdown cells, shortcuts, and essential tools like Drive, secrets, and shell commands.

Explore how to create and assign Python variables, perform basic calculations like total salary with a bonus, and manage data types such as integers, strings, and booleans in data analysis.

Explore how objects, variables, functions, classes, methods, and attributes interact in Python, inspect types with type, id, and help, and design simple classes with methods like display info.

Explore Python data types, from int and float to string and boolean, and learn to inspect, convert, and use basic operations with built-in types using type and help.

Master Python strings by defining them with quotes, using upper and lower, applying replace and split, and recognizing string methods, keyword and positional arguments, and operator overloading for data analysis.

Learn five string formatting operations in Python, including concatenation, the format method with args and kwargs, f-strings, old-style percent formatting, and join, with hands-on examples.

Explore arithmetic, assignment, and comparison operators in Python, including string operations, order of operations, and augmented assignment, with practical examples in a Jupyter notebook.

Learn how to use Python conditional statements with if, elif, and else, control flow through boolean conditions, indentation, and common mistakes in comparisons, including skill and experience checks.

Master lists, the first of Python's container types, denoted by square brackets, and learn methods like append, remove, pop, and slicing to manipulate ordered data.

Explore how Python dictionaries store key-value pairs with hashable keys and mutable values, using examples like databases and languages. Master common operations: get, keys, values, pop, and update.

Discover sets in Python, a mutable, unordered collection defined with curly braces that stores unique values and supports add, remove, and pop operations, with no indexing.

Explore tuples, defined by parentheses, as fixed, indexable sequences you can slice, creating stable item sets in Python. Learn that modifications create new objects, unlike lists.

Apply Python's logical operators (and, or, not) and explore membership (in, not in) and identity (==, is) tests on booleans and lists, using work from home, health insurance, and degree as examples.

Discover how Python loops—from for to while—help data analysts apply the dry principle, iterate over lists and dictionaries, and extract targeted data like data analyst jobs.

Explore list comprehension to generate and transform lists from ranges, replace loops and append, and adapt to lists, sets, tuples, and dictionaries with optional conditions.

Explore a practical Python exercise that filters data analyst and business intelligence analyst roles by required skills, using loops and booleans to match Python, SQL, and Excel.

Discover built-in and user-defined functions in Python for data analysis, including defining, calling, returning values, and using min, max, sum, sorted, and basic libraries like pandas, NumPy, and matplotlib.

Explore lambda functions, anonymous one-line operations in Python, and their use with list comprehensions and built in functions like filter to simplify data analysis tasks.

Explore creating and using Python modules and functions, import techniques (import vs from ... import), and docstrings, with examples like a salary calculator and statistics functions.

Learn to clean a csv dataset from hugging face by converting the job date to datetime objects and parsing the job skills into a list using python's standard library.

Explore how third-party libraries accelerate data analysis by using pandas to read csv files into data frames and visualize data with matplotlib, numpy, and seaborn.

Learn to implement a simple list class and a base salary class in Python, with methods for adding items, computing total salary and bonus, and formatting outputs.

Explore numpy introduction and its array operations, compare mean calculations with python's statistics on a million salaries, and see how numpy underpins pandas and matplotlib for faster analytics.

Explore pandas, the industry standard for tabular data, to read csv and excel files, create pandas dataframes, and manipulate data efficiently in Python.

Explore pandas basics by inspecting, cleaning, and analyzing data; learn to view data with head and tail, select columns, use iloc, and filter with boolean conditions.

convert the job posted date to datetime, derive the month, and sort by date; drop salary hourly average and remove nulls from salary year average in place.

learn to clean and inspect data with pandas, perform aggregations, group by operations, and compute min, max, median, and value counts across job titles and countries.

Explore pandas basics for data analysis by using group by, aggregation, and sorting to analyze US job salary patterns, clean data, and prepare for matplotlib plotting.

Learn to visualize analytics with matplotlib, explore plot types and cheat sheets, and customize figures using pyplot and numpy for sin waves.

Plot job postings over time with a line chart and display monthly counts of job titles using pandas, matplotlib, and value_counts.

Learn to clean and label Matplotlib visualizations by setting titles, axis labels, and rotated x-axis ticks with proper alignment, using a stateful plotting approach.

Learn to plot data quickly with pandas plotting and matplotlib, using bar and line charts on series and data frames, specifying x and y, and removing Na values.

Explore Matplotlib basics by visualizing median salaries by job title using group by and median aggregation, then compare with means through a histogram and a horizontal bar chart.

install python via the anaconda distribution on Mac or Windows, verify with python --version, and understand conda environments and the Anaconda Navigator, setting up for code editing with VS Code.

Explore how to create and manage Python virtual environments with conda and Anaconda, activate kernels in VS Code, and install pandas, datasets, and matplotlib for data analysis projects.

Learn to access and slice data in pandas using the loc method, replacing iloc, as you load datasets, organize notebooks, and clean data by handling missing values for analysis.

Clean large datasets in Python using pandas by filling missing salary values with medians, copying data frames, and removing duplicates based on job title and company name from 700k rows.

Master the sample and copy methods for data management in pandas, including generating random samples with a fixed random state, and safely duplicating frames to fill NaN values with median.

Explore pivot tables in pandas to compute median salaries for data analysts, data engineers, and data scientists by country, identify six top countries, and visualize results with a bar chart.

Master index management in pandas by learning reset index, set index, and sort index to clean and organize data frames after filtering and pivoting.

Analyze US job postings over 2023 by month using pivot tables, extracting months from posted dates, and plotting top three titles with a line chart for clear trends.

Learn to merge data frames in pandas by joining on common keys like company name, then visualize trends across data science and tech job postings.

Learn to concatenate dataframes in pandas using pd.concat to stack monthly datasets (January, February, March) into a master dataset, preserving or resetting indices as needed for data analysis.

Learn how to export a pandas dataframe to csv and excel, manage indexing and file locations, and explore parquet, sql, and pickle options.

Explore how to use the DataFrame apply method to transform columns and rows, convert string lists with ast.literal_eval, handle NaN, and compute inflation-adjusted salaries.

Learn how to use the explode method to convert skill lists into individual rows, group by job title, count top skills with value counts, and visualize them with bar charts.

Apply pandas explode, apply, and pivot to analyze monthly trends of data analyst skills, then create a pivot table and line plot to visualize counts.

Create multi-plot figures with matplotlib subplots and tight layout to compare metrics across categories. Use pandas to count job titles and skills, then render bar charts on a single figure.

Learn how to plot pie charts for boolean data, customize with value_counts, start angle, auto percentage formatting, and labels, and create multiple subplots to compare true vs false.

Discover how scatter plots reveal correlations between skill demand and median yearly salary in data analyst roles, highlighting Python, SQL, Excel, and Tableau.

Explore advanced line chart customization and scatter plot upgrades in Python data analysis, tuning line width and style, color maps like viridis, and adjust text for readability and y-axis dollars.

Explore how histograms visualize salary distributions, adjust bins, apply x-axis limits, and label axes to reveal right-skewed data in United States data analyst salaries.

Compare salary distributions across data analyst, data engineer, and data scientist roles in the United States using box plots, exploring median, interquartile range, whiskers, and outliers.

Explore salaries for data analysts in the United States by identifying the top paying skills and the most in-demand skills, using matplotlib plots of median salaries and counts.

Learn to create compelling visualizations with Seaborn, built on Matplotlib, using hue-based coloring, palettes, and themes to enhance histograms, box plots, and other plots.

Build final project visualizations in Python to showcase data analysis expertise, using EDA with Matplotlib and Seaborn on US job postings, skills, and top data analyst roles.

Master git and GitHub to coordinate code across teams. Initialize a local repository, configure user details, and push changes to a remote GitHub repository using VS Code.

Identify and quantify the most in-demand data skills for data analyst, data scientist, and data engineer using Python, matplotlib, and seaborn, with percent-based visual insights.

Analyze trends of top data analyst skills in US monthly job postings, convert counts to percentages, and visualize SQL, Excel, Python, Tableau, and Power BI.

Analyze median salaries of top data jobs in the United States using box plots, comparing data scientist, data engineer, and data analyst, and highlight top paying and popular skills.

Explore optimal data analyst skills by converting salary insights into percent-based visualizations, color-coded by tools and technologies, and identifying top priorities for US job postings.

Upload your Python data analysis project to GitHub, update the readme with sections on introduction, tools, analysis, insights, and wrap-up, and reflect on challenges and visualization.