Data Analysis with Python

Explore Python basics and Jupyter notebook setup by learning variables, data types int, float, bool, str, and installation steps for Anaconda. Apply practical exercises to reinforce these concepts.

Practice includes installing and using Jupyter or Anaconda, creating and renaming Python notebooks, running code cells, and exploring data types, printing, and simple arithmetic.

Continue the module two lab on Python basics, covering data types and type casting in Jupyter notebook; convert between int, float, bool, and string, and use print with newline.

Explore basic Python syntax, covering arithmetic, logical, comparison, and assignment operators, plus string manipulation methods like capitalize, count, endswith, find, and replace, then apply in a practical exercise.

Engage in practical python syntax with arithmetic operations, including float and floor division, exponent, and remainder, then apply comparison operators and string handling techniques like lower, strip, and length.

Explore core Python syntax with practical exercises on logical operators (and, or, not), assignment operators, and membership and identity operators, building foundations for data analysis with Python.

Develop practical Python string skills with length, indexing, slicing, and negative indexing. Learn multi-line strings with triple quotes, and apply methods like upper, lower, title, and f-strings for formatted outputs.

Master core Python syntax by exploring string manipulation with replace, password encryption concepts, and robust arithmetic operations, including type conversion and using the math module for ceil, floor, and sqrt.

Master Python conditional programming with if, elif, and else, indentation, and practical exercises on comparing values, even-odd checks, voter eligibility, leap year logic, using format or f-strings and input validation.

Develop conditional programming in python using if/elif/else, practice leap-year logic, implement for loops with range to build multiplication tables, and explore password validation criteria.

Explore practical conditional programming in Python using for and while loops to validate passwords (length and character types) and control flow with break and continue, with examples.

Explore Python functions, including built-in and user-defined types, recursion and lambda forms, and function arguments, defaults, and globals. Inspect sequences: lists, tuples, sets, and dictionaries, covering mutability, order, and indexing.

Explore Python functions and sequences through a hands-on session, covering built-in functions, function references, user-defined functions, default and keyword arguments, returns, and a homework counting uppercase, lowercase, and digits.

In this continued practical session of module four, learners implement a function to count upper, lower, and digit characters, and explore global and local scope, lambda, recursion, and factorial basics.

Data analysis with Python: explore Python sequences, focusing on lists and tuples (with upcoming sets and dictionaries) and mastering list declaration, indexing, slicing, reversing, and common operations.

Master Python sets and dictionaries: Create blank sets, avoid ambiguity with curly braces, and use add, update, and difference update. Practice iterating and sorting by keys and values.

Learn to sort dictionaries by keys and by values in Python using sorted with a lambda, and apply map, filter, and reduce to manipulate sequences.

Explore object oriented programming concepts in Python, including classes, objects, attributes, methods, inheritance, and polymorphism, then dive into numpy for multi-dimensional arrays and key operations.

Offers a practical session on object-oriented programming, teaching class and object creation, attribute access with self, and constructors including default constructors.

Explore inheritance and polymorphism through a school example in Python. Learn to implement parent and child classes, and use super or parent class calls to access base attributes.

Explore numpy practicals with numpy ndarrays, data types, and memory efficiency. Learn to create arrays, set dtype and itemsize, and generate values with arange and linspace.

Explore numpy arrays, including 1d to 3d structures, slicing, indexing, reshaping, and element-wise operations, with practical examples on shape, random data, and basic functions.

Explore numpy array operations and practical module usage in Python, including os for directory handling, listing files, renaming and removing files, and date time for runtime measurements and custom formatting.

Learn practical pandas basics with a focus on series, including creation, indexing, and common operations, and compare pandas series with numpy arrays while handling versions and installations.

learn to build pandas dataframes from lists or dictionaries. access data with loc, iloc, or bracket notation, and handle missing values with isna or isnull, and simple operations like sum.

Learn practical pandas data frame manipulation, inspecting memory usage and data types with info, changing data types for optimization, creating and updating columns, filtering, transposing, and removing duplicates.

Explore Python file handling: create and modify text files with open, write, and append modes; read CSV and text data, navigate with seek and tell.

Learn to convert unstructured text to structured data by reading with pandas, using read_clipboard, read_csv, read_excel, and read_json, and clean data for data frames.

Learn to read data into a data frame from clipboard, excel, and csv using delimiters and headers. Explore reading json with the json module, including transpose to correct the data.

Learn practical data cleaning and preprocessing by removing irrelevant columns, handling missing values, dropping duplicates, and applying try and accept exception handling to keep analysis robust in Python.

Learn practical data cleaning and preprocessing in python using pandas, including date parsing, handling nulls, trimming spaces, regex-based string cleaning, and outlier removal with quantiles for reliable time series analysis.

Master pandas group by and aggregation with sum, mean, min, and max; learn joins inner, left, right, and outer to merge dataframes. Explore pivot, unpivot, and column-wise concatenate.

Master pandas techniques for data analysis with practical exercises on describe, group by, pivot and pivoting, concatenation, and merge and join operations, plus exporting results and managing indices.

Master pivoting and unpivoting with pandas, turning long sales data into a wide format and back with melt, and learn to concatenate frames for seamless data analysis.

Analyze a data assignment by cleaning e-commerce order data, computing expected courier charges with weight slabs and zone charges in a jupyter notebook, and flagging undercharged or overcharged orders.

Learn data cleaning and preprocessing in Python, including null handling, forward/backward fill, mean/median/mode imputation, and exploring footfall to sales relationships for better data quality.

Explore assignment solution for data analysis with python: clean data, remove duplicates, merge tables, calculate shipment weights, slabs and charges, and export results to multiple excel sheets.

Explore exploratory data analysis with Python (EDA) using seaborn and matplotlib to visualize distributions, perform univariate, bivariate, and multivariate analyses, and assess skewness, kurtosis, and sampling effects.

Explore exploratory data analysis techniques beyond visualization, including sampling, univariate and multivariate analyses, time series, bar and pie charts, and correlation heatmaps.

Learn to gather data via an API using Python: request data with a GET call, handle JSON, extract articles, and convert results into a pandas DataFrame for analysis.

Explore linear algebra concepts with numpy, including matrix rank, determinant, inverse, eigenvalues, and eigenvectors, plus complex numbers and dot products for data analytics in Python.