Course Description
This comprehensive course is designed to equip students with the knowledge and skills necessary to perform advanced data analytics using a variety of techniques and tools. Students will gain hands-on experience in data manipulation, visualization, and interpretation, as well as learn how to apply statistical and machine learning methods to extract meaningful insights from complex datasets. .
What you’ll learn
- Using Python for Data Analytics
- Data Analysis with NumPy
- Data Manipulation with pandas.
- SQL for Data Analytics.
- Power BI Essentials
- Tableau
- Excel
Data - Data and it's types, data formats ,data sources and datasets
Data Analytics - Data Analytics, types of data anlytics, steps in data analytics
Data Collection - Primary data collection and secondary data collection, primary data collection using questionaire, webscraping and export data from databse to excel
Data Cleaning - Data cleaning using excel, outliers, scaling data and excel functions
Data Visualization - Basic data visualization using excel
Introduction to Python - Overview of Python programming language Setting up python development environments
Fundamentals of Python - Understanding Statements, Expressions and Indentation, Overview of Identifiers, Keywords and Comments Variables, Common Data Types, Type Casting and Conversion, Operators in Python, Hands-on Activity
Loops, Functions & Error Handling - Conditional statements, Looping constructs, Loop Control Statements: Break, Continue and Pass Defining and Calling Functions, Function Parameters and Return Values, Recursive Functions Introduction to Exception
Data Structures in Python - Lists & Manipulation Techniques, Slicing and Indexing in Lists, Tuples & Operations on Tuples, Slicing And Indexing in Tuples Common Operations on Both Lists and Tuples, Dictionaries & Manipulating Dictionaries, Sets & Manipulating Sets Common Operations on Both Dictionaries and Sets, Hands-on Activity
Introduction to Object-Oriented Programming - Classes and objects, Attributes and methods, Constructor and destructor methods, Inheritance and polymorphism
File Handling, Working with Libraries and Packages in Python - Reading from and writing to files, File modes and operations, Handling file exceptions, Introduction to external libraries and packages, Using pip for package management, Installing and importing libraries
Introduction to NumPy - Overview of NumPy and its features, Installing NumPy and setting up the development environment, Introduction to NumPy arrays: creation, indexing, and slicing, Creating multidimensional arrays
Array Manipulation - Reshaping and resizing arrays, Stacking and splitting arrays, Broadcasting and vectorization
Mathematical Operations - Element-wise operations, Universal functions (ufuncs) in NumPy, Aggregation and reduction operations
Data Analysis with NumPy - Loading and saving data using NumPy, Statistical analysis with NumPy, Data manipulation and filtering
Introduction to pandas - Overview of pandas and its benefits, Installation and setup, Introduction to pandas data structures: Series and DataFrame
Data Manipulation with pandas - Creating pandas Series and DataFrames, Indexing and selecting data, Filtering and sorting data
Data Cleaning and Preprocessing with pandas - Handling missing values: dropping, filling, interpolation, Removing duplicates, Data transformation and reshaping
Data Aggregation and Grouping - Grouping data with pandas, Aggregation functions: sum, mean, count, etc, Combining and merging datasets
Time Series Analysis & Visualizations with pandas - Data visualization using Matplotlib (basic plots, customization, EDA) Handling time series data with pandas, Resampling and frequency conversion, Time-based indexing and slicing
Univariate Analysis with pandas - Descriptive statistics: mean, median, mode, variance, standard deviation Distribution analysis: histograms, density plots, box plots, Summary statistics and visualization for single variables
Multivariate Analysis with Pandas - Correlation analysis: Pearson correlation, Spearman correlation, Scatter plots and pair plots for exploring relationships between variables, Heatmaps for visualizing correlation matrices
Working with SQL - What is SQL? Difference Between RDBMS and DBMS, Difference between SQL and NoSQL, Setting Up the Environment (MySQL), SQL Basics - DDL Commands, DML Commands, DQL Commands, String Operations, Ordering the display of tuples, SET Operations, Aggregation in SQL, Grouping in SQL, HAVING, EXISTS, ANY, CASE, SubQueries, Set Membership, Performing Join Operations
Querying SQL Databases with Python - Stored procedures, Executing SQL queries from Python
Introduction to Power BI - Overview of Power BI, Installation and setup, Understanding the Power BI interface, Introduction to Power BI Desktop.
Data Importing and Transformation - Connecting to various data sources (Excel, databases, etc.), Data loading and transformation using Power Query Editor Cleaning and shaping data for analysis
Data Modeling - Introduction to data modeling concepts, Creating relationships between tables Implementing calculated columns and measures
Visualizations, Dashboards and Report Creation - Overview of data visualization principles, Creating basic charts (bar, line, pie, etc.), Using advanced chart types (treemaps, waterfall, etc.) Formatting visualizations, Designing interactive dashboards, Publishing and sharing reports on Power BI Service Managing access and permissions
Data Analysis with Power BI - Introduction to DAX (Data Analysis Expressions) Writing DAX formulas for calculated columns and measures Performing data analysis using DAX functions
Case Studies - Analyzing Customer Churn, Competitor Sales Analysis, Analyzing Healthcare Data
Introduction to Tableau - Overview of Tableau and its features, Installation and setup, Understanding the Tableau interface Introduction to Tableau Desktop and Tableau Public
Data Connection and Preparation - Connecting to various data sources (Excel, databases, etc.), Data loading and preparation in Tableau Cleaning and reshaping data for analysis
Visualizations and Dashboard Creation - Creating basic charts (bar, line, pie, etc.) in Tableau, Formatting and customizing visualizations Implementing basic calculations and parameters, Using advanced chart types (treemaps, heat maps, etc.) Implementing dual-axis and combined charts, Creating interactive dashboards with actions and filters, Designing dashboards for different audiences and purposes
Data Analysis with Tableau - Introduction to Tableau calculations (Table calculations, LOD expressions), Using calculated fields for advanced analysis Performing statistical analysis and forecasting in Tableau.
Geographic Analysis - Visualizing geographic data in Tableau (maps, spatial joins, etc.) Customizing maps and adding layers Analyzing spatial patterns and trends
Case Studies - HR Analytics, Analyzing Job Marketing Data, eCommerce Analysis
Introduction to Excel for Data Analytics - Overview of Excel as a data analysis tool, Introduction to Excel interface and navigation, Understanding worksheets, rows, columns, and cells
Data Importing and Cleansing - Importing data from different sources (text files, CSV, databases, etc.) Cleaning and preprocessing data using Excel's built-in tools (text to columns, find and replace, etc.)
Data Transformation and Preparation - Using Excel's functions and formulas for data transformation (IF, VLOOKUP, INDEX-MATCH, etc.) Combining and restructuring data using PivotTables
Data Visualizations - Creating basic charts (bar, line, pie, etc.) in Excel, Customizing and formatting charts for effective visualization Building interactive dashboards using Excel features (Slicers, PivotCharts, etc.)
Advanced Data Analysis - Performing advanced calculations using array formulas Using Excel's statistical functions for descriptive analysis (mean, median, standard deviation, etc.) Conducting regression analysis and trend forecasting in Excel
PivotTables and Power Pivot - Advanced data analysis with PivotTables and PivotCharts, Introduction to Power Pivot for data modeling and analysis Creating relationships and measures in Power Pivot
Case Studies - Supply Chain Analysis, Sales Data Analysis
Google Datastudio - Google Datastudio -advantages & disadvantages
Create Report - choosing a template, connecting datasources, choosing metrics that matter,sharing reports.
Analyzing Job Marketing Data
Competitor Sales Analysis
Analyzing Healthcare Data
Project - Do a Project using the learned skills and present the output
Our Student Reviews
4.5
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Frequently Asked Questions
Data analytics involves the process of examining data sets to draw conclusions about the information they contain, typically with the aid of specialized software tools like Tableau, Power BI, Excel, and Python. . Data analytics enables us to transform raw data into actionable information, guiding strategic decisions and fostering innovation across various industries from healthcare and finance to marketing and beyond. In today's digital age, proficiency in data analytics is essential for professionals seeking to leverage data effectively and contribute to organizational success.
Yes, individuals from any background or domain can study data analytics. While a background in fields like mathematics, statistics, or computer science can be advantageous, this data analytics course is designed to accommodate beginners with no prior experience. The key requirements are curiosity, a willingness to learn, and an interest in working with data to derive insights and make informed decisions.
Programming skills are not always a strict requirement to start learning data analytics, but they can significantly enhance your ability to excel in the field. Ultimately, proficiency in programming empowers you to leverage data analytics tools more comprehensively and pursue advanced career opportunities in data-driven fields.
Proficiency in Tableau, Power BI, Excel, and Python opens doors to roles such as data analyst, business intelligence analyst, financial analyst, marketing analyst, or data scientist, depending on the specialization and industry focus.
There are tons of opportunities. Companies are realizing that they need to leverage their data to understand how to improve their businesses on every scale! From internal process, external processes, improving customer satisfaction, understanding what new products and services to offer, etc. Companies who fail to utilize their data are and will be not be competitive, therefore they will be left behind. Many companies are in the beginning stages of sorting, managing, and analyzing their data. Therefore, they need data analysts, data engineers, and scientists to help them with that.
When considering this career path, math is one thing to be concerned about . There are other essential characteristics as well. Regardless of their function in this sector, logical thinking, curiosity, It is not the only thing to be concerned about. Basic statistical knowledge would be beneficial in a data analyst job role; nevertheless, the nature of employment, not the title, is more important.
The use of analytics in decision-making is critical. As data analysts, professionals will have the potential to influence business strategy and the way the company moves forward. Working closely with important stakeholders and using their experience to advise on the best course of action, they get to the heart of difficult business challenges. In this way, they will have a direct impact on the company’s performance, which is a tremendously satisfying position to be in.
Before getting started, ask yourself What role do I want? and What is my starting point?. These will determine which skills you should acquire first, and help you plan your learning path. With time, as your understanding deepens and you explore more tools, you will find more things to discover and deep dive into. Once you get started, learning never ends. If you love learning and being reset to “level 0” once in a while, it’s a great path that satisfies this need. If you don’t, it may sound terrifying.
5hr
2hr