Data Analysis using Python and R for Absolute Beginner

Install the Anaconda distribution on Windows 64-bit, download the graphical installer, and complete the setup with license and destination folder, then launch Jupyter notebook and Spyder.

Learn basic Python programming by writing the first lines of code in a Jupyter notebook, exploring variables, arithmetic operations, string methods, and simple boolean logic.

Explore Python basics for beginners with string handling, including indexing and slicing, concatenation with plus, printing outputs, and converting numbers to strings using str.

Learn how Python lists work, using square brackets and commas to hold items of various types, including nesting and zero-based indexing.

Explore Python lists, including creating, indexing (positive and negative), adding and removing elements, and converting strings to lists; learn split and join for basic natural language processing.

Introduce tuples as immutable containers for multiple data types, alternatives to lists; convert a tuple to a list to modify, and note parentheses indicate tuples while lists use square brackets.

Understand tuples as data containers for city information such as latitude and longitude, and learn how changing a tuple raises an error while converting to a list enables edits.

Explore how dictionaries store data as key-value pairs using curly braces, with examples like name and age, and recognize their role in Python data analysis.

Explore Python dictionary by building biodata, employee data dictionary, storing and fetching values with keys, using get with defaults, and updating or deleting keys.

Learn how Python sets are unordered collections of unique, immutable elements, how converting a list to a set removes duplicates, and how to use union, intersection, and difference.

Master if else conditions in Python to make decisions, using simple if, if-else, and nested if structures with user input.

Learn how Python loops process a sequence using for and while constructs, applying conditions and indentation to perform calculations on number lists until conditions are met.

Master for and while loops in Python for data analysis, using range to generate sequences, and iterating over strings and lists with enumerate to access indices and items.

Discover how Python comprehensions offer an elegant, single-line alternative to for loops by transforming a sequence into outcomes, and compare old versus new formats.

Learn Python list and dictionary comprehension with practical examples: square numbers from a range, single-line code, and nested loops to extract words from sentences.

Learn how functions drive modular code, simplify maintenance, and enable collaboration by letting multiple developers merge their code, using def with parameters and return or no-return formats.

Learn how to define and call Python functions, pass parameters, implement recursion with a factorial example, and use variadic arguments to sum multiple values.

Explore the map, reduce, and filter concepts in data analysis with Python and R, learning how to apply a function to list elements and break down processing into pairwise steps.

Learn to use map, filter, and reduce with lambda functions in Python to transform lists, compute squares, and selectively sum results. Tackle string operations like capitalizing initials and joining names.

Explore how NumPy enables efficient data analysis and machine learning tasks for large datasets by vectorizing operations over millions of records, delivering faster performance than traditional for loops.

Explore numpy, the numerical Python library, and learn to create arrays from lists, distinguish arrays from lists, and perform vectorized elementwise operations such as multiplication and squaring without explicit loops.

Explore initializing and manipulating arrays in Python, including lists and shapes (rows and columns), floating-point elements, and random numbers, with help, Google and Stack Overflow guiding each function.

Analyze a multi dimensional area by inspecting shape, memory, and access patterns; learn indexing and slicing of rows and columns, and compare for loop versus vectorized operations for speed.

Learn basic NumPy array operations, including reshaping into 3x4, 4x3, or 4x4, and using transpose and dot to rearrange data. Explore vertical and horizontal stacking and vectorized functions.

Explore the world of pandas as the essential library for data gathering, statistics, and visualization, including computing medians and averages for bar charts.

Explore how pandas series and dataframes organize data, create numeric and character series, understand index, and build data frames from key-value pairs and external files.

Load market data with pandas, explore data types and shape, and sort by profit and sales. Learn to slice and select rows and columns by name or index.

Explore dataframe indexing techniques in this session by applying operation based indexing with iloc, and label based indexing with loc to select rows, columns, and boolean filters.

Merge multiple dataframes from market data and dimension files using indexing and level indexing, then perform aggregations, pivot-like sums, and arithmetic calculations with handling of nulls.

Merge multiple data frames, group by category and subcategory, and compute aggregations like sum and mean. Apply functions to create new columns, round values, and pivot data for insights.

Learn to extract data from diverse sources, including text files, RDBMS databases, CSV files, and social networks, using Python techniques and basic web scraping for data-driven decision making.

Learn to extract data from delimited files and RDBMS sources and convert results to a data frame for analysis, including tab-delimited text, Oracle, and MySQL.

learn to scrape website data by inspecting html, extract job listings by title, company, and location with bs4, and read pdf files using PyPDF2.

Master data cleaning to prevent wrong outputs by replacing missing numerical values with mean or median, replacing missing categorical values with the most frequent value, and removing rows as needed.

Master imputing missing values in Melbourne real estate data with python, using mean or median, handling outliers, and applying percentile rules to maintain data integrity.

Impute missing values in Melbourne real estate data using mean, median, or mode, then replace latitude and longitude with the mean and fill remaining fields with the most frequent value.

Explore the world of visualisation and turn sales data into actionable insights. Use heat maps and scatterplots in data analysis with Python and R to reveal trends and predict sales.

Explore plot types for data visualization: bar charts for categorical versus numerical data, histograms for distributions, line charts for trends, scatterplots for relationships, and pie charts with limitations, including outliers.

Plot and customize with matplotlib by creating X and Y data, labeling X and Y axes, setting a title, using colors and markers, and building subplots, bar charts, and histograms.

Explore marketing data visualization with Matplotlib boxplots, histograms, and scatter plots, illustrating distribution, medians, outliers, and the impact of log transformation on sales and profit.

Explore Seaborn basics to beautify visualizations with boxplots, histograms, density plots, and scatter plots, using real data like sales and shipping costs to reveal distributions and relationships.

Explore seaborn’s correlation matrix to examine relationships among numerical variables like sales, discounts, and profit. Interpret positive and negative correlations and note the sales histogram for growth insights.

Merge marketing and sales data to create bi-variate aggregations by product category and customer segment, using bar charts, boxplots, histograms with log-scaled sales.

Learn to prepare time series data, group by day, and aggregate sales before plotting heatmaps by year and month to visualize seasonal trends for data analysis with python and r.

Explore car data through exploratory analysis to understand features, relationships, and price drivers, then upload the dataset, inspect columns, and begin plotting insights in Python.

Explore car data through data manipulation and analysis using Python and R, grouping by symbolic values, examining distributions and medians, and visualizing price trends with bar charts and scatterplots.

Explore data manipulation and analysis with plots, box plots, and heatmaps to study car attributes. Show how price links to horsepower and weight, and note limits when plotting many columns.

Identify and handle missing values, then extract and standardize the car company name by splitting the field, applying a lambda, correcting misspellings, and backing up before dropping the original column.

Outline the business objective and strategy, identify variables that influence car price, and use density curves, scatterplots, heat maps, and bar plots to present key insights to business users.