Python + Data Science : Practical Guide [13 Hours]

Learn Python and data science from the ground up, covering inferential statistics, linear regression, data visualization, web scraping, NLP, and introductory machine learning with hands-on projects.

Install Python 3.x with Anaconda and a code editor, access Python from the command line, and explore Jupiter notebook for prototyping and learning resources like Stack Overflow and Python docs.

Explore real-world data science through practical examples, including a data scientist’s LinkedIn-style resume and an inspiring word-usage project. Review a skills map spanning fundamentals, statistics, programming, visualization, and machine learning.

Learn how Anaconda acts as a package manager and virtual environment builder, and how Jupyter notebooks provide a browser-based interface for Python coding.

Master variables in Python by learning how to declare containers, use the assignment operator, and store integers and strings, enabling dynamic operations and accurate type checks.

Learn how integers and floats work in Python, perform basic arithmetic with plus, minus, multiply, divide, modulo, and exponent operators, and understand type interactions and simple type conversion.

Explore strings in Python, learn slicing and indexing, handle special characters, and apply string functions for basic text analysis and natural language processing.

Learn string handling in Python, including creating strings with quotes and escape characters, and using print. Discover string methods like lower, upper, count, and replace, plus indexing and slicing.

Learn to format strings dynamically in Python with the modulo operator, replacing placeholders with computed values for cleaner, multi-variable outputs.

Develop practical python skills by manipulating objects and data types, performing operations and assignments on integers, floats, and strings, and using type and help functions to read documentation.

Explore Python basics from the terminal, including variables, numeric operations, and type casting, then work with strings using strip and title while navigating help and built-in functions.

Discover how booleans and comparison operators drive conditional logic in Python. Use true/false values, relational operators, and if statements to control code execution.

Learn how if and else statements drive conditional logic in Python, using indentation and colons to execute code when conditions are true or false, with practical code examples.

Elif and logic operators teaches how to write complex conditional logic in Python using if, elif, else, and, or to create dynamic multi-condition statements with proper indentation.

Learn how try and except statements manage errors in Python, catch specific exceptions with multiple blocks, and use the traceback module to diagnose failures and explore finally and else.

Master finally, try-except-else chaining and how finally runs always. Learn to use the traceback module to capture exceptions as objects for later handling.

Explore common Python exception types and how to interpret and debug errors. Cover syntax, indentation, type, value, IO errors, and zero division errors, with practical debugging approaches.

Build a multi-branch if/elif/else program using user input about a lucky number. Validate input is an integer and apply rules for numbers over 20 and for even or odd numbers.

Use input to collect a lucky number and assign it to a variable. Build integer-validated checks with if/elif/else and try/except, plus modulo to test odd or even.

Learn how lists and tuples work in Python, including indexing, appending, and the mutability differences, with a look at dictionaries and looping for data science automation.

Explore Python dictionaries, their key–value structure, and how to initialize, access, and modify them, including keys(), values(), and items() for data manipulation.

Learn how to use for loops in Python to iterate over lists, dictionaries, and strings, understand indentation and syntax, and access values during iteration.

Master while loops in Python by building conditional logic with if statements, preventing infinite loops with break conditions, and comparing their behavior to for loops using real examples.

Integrate for loops with if statements and loop controls—continue, break, and pass—using the range function to build finite sequences, and learn to write dynamic, fast Python code.

Explore Python list and dictionary comprehensions to build lists and dicts in one line, using for loops with embedded if/else and enumerate to create index-value pairs.

Combine for loops, while loops, and if statements to build Python guessing game. Use the random module to pick a number 1 to 25, track guesses, and give higher/lower feedback.

Develop a Python guessing game where the computer uses random to pick a number from 1 to 25, the player guesses with higher/lower feedback, and track attempts across rounds.

Learn to script in Python by navigating folders in Bash on Mac, create directories, and run Python scripts, using Sublime Text or Adam to streamline debugging.

Learn to build a Python function with syntax, parameters, and a return value, using an add-10 example. Discover passing variables, calling the function, and using comments and print for debugging.

Explore how Python handles function parameters with *args and **kwargs and the role of scope in global versus local variables. Learn to pass flexible arguments while avoiding name conflicts.

Learn how to manage Python packages with pip, import libraries and modules, install official and unofficial libraries from repositories like GitHub, and explore practical examples with pandas.

Learn to read a csv file into memory with python, use the with statement for safe line-by-line handling, then manipulate and write results to another file.

Learn to create, write, and safely close files in Python, using with statements, join and map to convert data to strings, and produce tab or comma separated outputs.

Create three functions to load, add, and save a recipe book stored as a dictionary, using recipe names as keys and ingredient and value pairs as data.

Walks through building a recipe dictionary in Python, loading or creating a recipe book, collecting input for recipes and ingredients, using while loops, and saving updates with dumps.

Explore pandas data frames in Python, loading data, indexing and slicing rows and columns, and iterating for basic calculations and statistics.

Extend your pandas skills by mastering advanced indexing, conditional selection, and replacing values in dataframes. Learn to append and concatenate dataframes and export to csv, laying groundwork for upcoming statistics.

Explore the four statistics data types—nominal, ordinal, interval, and ratio—and learn the basics of mean, median, and mode using Python and simple code examples.

Explore how variance and standard deviation shape the normal distribution, represented by the bell curve, and see how changes in mean and standard deviation affect the distribution of scores.

Learn to perform a t-test and linear regression in Python, interpreting independent and dependent variables, p-values, and the slope and intercept to analyze data and visualize results.

Organize simulated dice rolls with pandas data frames and perform a chi-square contingency analysis in python to compare a normal dice with a trick dice, then interpret p-values.

Roll a six-sided die 20 times with rand, convert results to a list, create a trick die landing only on five or six, and test with chi-square on an array.

Explore data visualization in Python using Matplotlib to inspect data before analysis, and learn to create histograms and scatterplots in Jupyter notebooks with Anaconda.

Explore plotting and styling with Matplotlib by manipulating figure and axes, the access parameter, customizing limits, ticks, and gridlines, and creating subplots to build styled graphs.

Explore how to visualize and style scatter plots, customize color, size, and opacity, and plot from a pandas data frame, linking concepts across chapters for practical data science.

Master bar plots as a histogram type and learn when to use them over other plots. Build Python bar graphs showing means and standard deviations across nominal or ordinal categories.

Explore linear regression on the iris dataset with pandas and scikit-learn, create a scatter plot with a regression line, and interpret p-value, slope, intercept, and r-value.

Perform a linear regression on the iris dataset using length and width as features, evaluate with the r-value and p-value, and visualize the regression line with plotting style adjustments.

Explore the basics of web scraping with Python, focusing on inspecting HTML and using developer tools. Learn to extract data from simple sites with Beautiful Soup and requests.

Learn to scrape a website using requests and BeautifulSoup, extract the HTML source, and convert it into a BeautifulSoup object. Use a loop to crawl multiple sites.

Fetch html with requests and parse it using a beautiful soup object. Extract data with find_all and class or id; save as json; note selenium as an advanced option.

Learn to script a website using requests and beautiful soup to extract company data—names, awards, year, and ratings—from IDs, then save up to 10 results to a comma-separated file.

Scrape a business site to extract company name, award name, year, and rank, then write results to a csv file using BeautifulSoup and requests across multiple pages.

Explore natural language processing with Python by learning core concepts like tokenization, lemma and stem, parts of speech, and practical NLP tools such as the NLTK toolkit.

Learn practical natural language processing with Python by analyzing Shakespeare's Macbeth using sentiment analysis, text denoising, and engram (n-gram) techniques, and explore TF-IDF for statistical text analysis.

Explore sentiment analysis with the Vader sentiment analyzer in natural language processing. Apply tf-idf scoring using scikit-learn to measure word novelty and support machine learning demos.

Perform natural language processing and sentiment analysis by comparing average sentiment across two text blocks using Vader, line breaks, and advanced string slicing.

Split the Gutenberg corpus into blocks by double line breaks, compute the average sentiment per block with a custom dictionary, and compare Caesar and Macbeth by mean sentiment.