In this module, you will learn how to use the most versatile Data Analytics tool—Microsoft Excel. We will explore everything from basic formulas to advanced functions that provide efficient analysis and reliable results, taking your Microsoft Excel skills to the next level. At the end of this module, you’ll be able to Create dynamic reports by mastering one of the most popular tools, PivotTables.

This module is designed to provide you with in-depth knowledge on these:

• Introduction to Data Analytics

• Getting started with Excel

• Data entry, Editing, and Formatting in Excel

• Using Formulas and Functions

• Worksheet Management

• Data Validation, Data Sorting and Filtering

• Conditional Formatting

• Introduction to Excel Charts

• Advanced Excel Charts

• Pivot Tables

• Excel Macros

In this module, you will learn how to use one of the worlds most robust business analytics tools that allows you to connect to over 70 data sources. You will understand the flow of using Power BI, from connecting to various data sources, importing these into Power BI, transforming the data and then presenting it effectively. At the end of this course, you will be able to build interactive dashboards and publish them to the web and mobile app.

This module is designed to provide you with in-depth knowledge on these:

• Set up and Introduction to Power BI

• Power Query for data transformation

• Data Visualization with Power BI

• Data Modelling and Data Analysis Expression (DAX)

• Setup and Integration with Power BI services

Everything uses a database, and MySQL is one of the most popular databases out there. It is free and Open Source; MySQL is a great database for just about everything. Likewise, Python is one of the most popular and powerful programming languages today. Pairing the two together is a powerful combination! In this course you'll learn the basics of using MySQL with Python. You'll learn how to create databases and tables, add data, sort data, create reports, pull specific data, and more.

This module is designed to provide you with in-depth knowledge on these:

• MySQL Get Started

• MySQL Create Database

• MySQL Create Table

• MySQL Insert

• MySQL Select

• MySQL Where

• MySQL Where

• MySQL Order By

• MySQL Delete

• MySQL Drop Table

• MySQL Update

• MySQL Limit

• MySQL Join

Write your first Python program by implementing the concepts of variables, strings, functions, loops, and conditions. Understand the nuances of lists, sets, dictionaries, conditions and branching, objects, and classes. Work with data in Python, including reading and writing files, loading, working, and saving data with Pandas. Data visualization plays an essential role in the representation of both small and large-scale data. In this Data Visualization with Python course, you will learn how to create impressive graphics and charts and customize them to make them more productive and more pleasing to your audience. You will gain expertise in several data visualization libraries in Python, namely Matplotlib and Seaborn to extract information, better understand the data, and make more effective decisions. Learn data visualization and best practices when creating plots and visuals Master basic plotting with Matplotlib. Generate different visualization tools using Matplotlib such as line plots, area plots, histograms, bar charts, box plots, and pie charts Understand Seaborn, a data visualization library in Python, and how to use it to create attractive statistical graphics. Understand Folium and how to use it to create maps and visualize geospatial data.

This module is designed to provide you with in-depth knowledge on these:

• System environment setup

• Python Basics

• Python Data Structures

• Python Programming Fundamentals

• Working with Data in Python

• Working with NumPy Arrays

• Introduction to Visualization Tools

• Basic Visualization Tools

• Specialized Visualization Tools

• Advanced Visualization Tools

• Creating Maps and Visualizing Geospatial Data

• Statistical Computing

• Mathematical Computing using NumPy

• Data Manipulation with Pandas

• Data visualization with Python

• Intro to Model Building

This section should cover the fundamental concepts of Machine Learning, including the definition of ML, its applications, and the basic workflow of ML projects. Topics to cover may include data preprocessing, feature engineering, model training, evaluation, and deployment.

Types of ML:

Explain the different types of Machine Learning, primarily

• Supervised Learning: Learning from labeled data with input-output pairs.

• Unsupervised Learning: Learning from unlabeled data to find patterns and relationships.

• Reinforcement Learning: Learning through interactions with an environment to achieve goals.

ML Libraries:

Cover two popular Python ML libraries for building ML models:

• TensorFlow: Explain its role in building neural networks and deep learning models.

• Keras: Emphasize its user-friendly API and its compatibility with TensorFlow for quick model prototyping.

Life Project - CV Models:

In this project, you'll work on building computer vision (CV) models. The steps involved may include:

• Data Collection: Gather a suitable dataset for the CV task.

• Data Preprocessing: Clean, augment, and prepare the data for model training.

• Model Selection: Choose appropriate pre-trained models or architectures for your CV task.

• Model Training: Fine-tune or train the selected models on your dataset.

• Model Evaluation: Measure the model's performance using relevant metrics.

• Deployment: If applicable, deploy the model to make predictions on new data.

Data Annotation:

Data annotation involves labeling or annotating data to train supervised ML models. This project could include:

• Learning about different types of annotations (e.g., bounding boxes, segmentation masks, etc.).

• Using annotation tools or libraries to annotate data for your CV project.

• Understanding best practices for data annotation to ensure high-quality training data.