The Complete Data Analyst Course: From Zero to Data Hero!

Explore the four stages of data analysis and the roles of data analysts and data scientists. Understand descriptive, diagnostic, predictive, and prescriptive analytics for data-driven decision making.

Learn the five data analysis stages from defining objectives to visualizing results, and explore data types, including categorical and quantitative, with subtypes such as binary, nominal, ordinal, discrete, and continuous.

Compare data analysts and data scientists in responsibilities, tools, career progression, and salary, highlighting day-to-day tasks with Excel, Python, SQL, machine learning libraries, and visualization tools.

Explore the basics of Microsoft Excel, including worksheets, workbooks, cells, and ranges, and navigate the Excel start page, ribbons, and tabs to organize and format data.

Enter and format payroll data in Excel to track employee names, hours worked, hourly pay, and tax rate, using autofill, column resizing, and bold, centered headings.

Calculate gross pay in Excel with formulas and functions, format currency, and use absolute and relative references, autofill, and functions like sum, average, min, max.

Learn to import data into Excel using the import wizard and CSV files, including delimiter options and basic transformations via Power Query Editor, while using Kaggle to find datasets.

Sort data in Excel using basic alphabetic and numeric sorts, then apply a custom multi-level sort by year and sales, while preserving headers and data integrity.

Master Excel filters to focus on key data by applying status and sales filters, using text and number filters, and clearing or toggling filters with shortcuts like ctrl+shift+l.

Explore goal seek and scenario manager in excel to analyze how changes in ticket price and attendance affect revenue and profit, using a music event dataset.

Apply conditional formatting in Excel to visualize sales data, highlighting high and low orders with rules like greater than 9000 or less than 1000, bars, color scales, and icon sets.

Master essential Excel tools for data analysis, including freeze panes, text to columns, remove duplicates and blanks, and special paste options like transpose and values only.

Use the Excel data analysis Toolpak to generate descriptive statistics for the price column. Grasp key metrics such as mean, standard deviation, skewness, kurtosis, and confidence level.

Explore Excel lookup functions—VLOOKUP, HLOOKUP, and XLOOKUP—on a movie dataset to retrieve gross revenue and company details, and learn backwards search and error handling.

Explore conditional functions in Excel, including if, nested if, and IFS, to categorize movie runtimes as short to very long and handle errors with if error, illustrated through ROI calculations.

Explore left, mid, right, and find text functions to extract day, zip code, month, and street data from a fictional customers dataset.

Explore Excel text functions, including trim, concat, len, and case functions, to clean data, join first and last names with a space, and adjust text case for consistency.

Explore the essential date and time functions in Excel, including date, day, month, year, today, now, date diff, week day, and week num, with a hands-on dataset.

Leverage countif, countifs, and countA to count orders and non blanks in a sales dataset, then apply sumif and sumifs to total by product line (motorcycles) and status.

Build sequences and dates with sequence and date functions, and find top or bottom items using large, small, and rank eq with ascending or descending order.

Learn to create data visualizations in Excel by building line, column, bar, scatter, and pie charts, and understand their benefits, potential pitfalls, and effective formatting.

Format and style Excel charts to improve readability by adding titles, data labels, and chart elements, and customize axes, styles, and data visibility.

Explore advanced chart types in Excel, including sparklines, box plots, histograms, and waterfall charts, to visualize trends, distributions, and cumulative totals across a dataset.

Master pivot tables in Excel to summarize data trends using IMDb data, comparing average scores by year and genre and analyzing the sum and average of gross revenue with drag-and-drop.

Create and format pivot tables in Excel, adding fields, calculating averages, applying conditional formatting, adjusting subtotals and grand totals, and using a slicer for dynamic data analysis.

Create and visualize pivot charts linked to pivot tables to explore movie data, showing average scores by year and genre while applying country filters.

Explore a complete data analysis project in Excel, from data cleaning to analyzing and visualizing HR analytics, culminating in an interactive dashboard with filters and pivot charts.

Learn to analyze and visualize data in Excel using pivot tables and pivot charts, build an interactive HR analytics dashboard with slicers, and explore attrition, education, salaries, and revenue.

Download and install Anaconda to obtain Python with data analysis libraries and the Jupyter Notebook, then launch Jupyter to work with ipynb notebooks locally.

Explore the Jupyter notebook environment, including Anaconda prompt access, kernel basics, and markdown, with essential shortcuts for running, saving, and editing notebooks.

Delivers Python basics for data analysis, covering data types, variables, printing, indexing and slicing, and essential string methods as groundwork for NumPy and pandas.

Explore Python data structures—lists, dictionaries, tuples, and sets—and their mutability, nesting, and memory behavior. Learn core operators, including comparison, logical, and membership, with practical examples in a notebook.

Explore if/else/elif statements, for and while loops, and essential Python functions. Learn about parameters, arguments, return values, and docstrings with practical examples.

Master lambda expressions and higher-order functions like map and filter to transform and filter data in Python, and learn how to import modules, alias them, and use the OS module.

Practice activities for the Python crash course cover data types, printing and variables, data structures, loops, and functions, culminating in a calculator project for add, subtract, multiply, and divide.

cover the complete data analyst course: Python practice activities solutions, highlighting data types, variables, f-strings, lists, dictionaries, operators, loops, conditionals, functions, lambda, map, and filter.