1.0 Where will Classes take Place?

The purpose of this guide is to help you in preparation for our online classes starting this

weekend (July 11). If you have any issues or questions, please reach out to

[email protected]

1.0 Where will Classes take Place?

The classes are live and instructor-led and will take place online via Zoom. (see section 3 below

for details on how to join the class)

2.0 What Time is the Class and What is the Structure?

The duration of the live class is between 10:00 am – 2:00 pm ET (US and Canada).

Please, ensure you are aware of the equivalent time in your area. For example:

• Nigeria/UK: 3pm – 7pm;

Here is a tentative planned structure for the Class

(**Note: this is subject to change as necessary)

Time

Segment

Activities

9:55am – 10:05 ET

10mins

Admission into the class

10:05am – 11:45am ET 1hr 40mins

Lecture

11:45am – 12:15pm ET 30mins

Break time

12:15pm – 2:00pm ET

1hr 45mins

Lecture / Hands-On Demo / Class

Exercise / Wrap Up

3.0 How Do I Join the Class?

You can join the class using any of the following options:

• Option 1: Use the class URL below

https://us02web.zoom.us/j/82848589228?pwd=SndtSzc4eXVpdkpqNFNEV2FPUHZFZz

09

• Option 2: Join through Zoom App/Desktop client using the Meeting ID and Password.

Meeting ID: 828 4858 9228

Password: 076848

o Open the Zoom desktop app.

o Click on the Home Button.

o Click Join and type in the provided Meeting ID, password and your name

o Wait to be admitted into the class by the admin

(Note: Please use your name in the login or use a login ID that we can match

with your name after the first class for admission into the class)

4.0 What if I miss a Class?

Not a problem. We would provide secure access to the lecture recordings and any project

or document shared in the class if you miss the live session.

5.0 What Are Some Best Practices While in Class?

• Try and stay signed in when in class.

• If you turn your camera on, have your camera at eye level

• Stay muted unless you're talking to reduce background noise.

• Make sure you sit in a well-lit and quiet place.

• Be mindful of what's going on behind you. Think about having solid wall behind you or

turning on the virtual background in Zoom.

• Avoid distractions (as much as possible).

• We would have some practice exercises / hands-on projects to validate your knowledge

in the concepts taught, due your very best to ensure you complete them.

• We will use the chat window to interact during the class. If you have question or need to

answer a question during the class, you can post on the chat window

• We would also have a WhatsApp group for the class where you can also chat in and off

the class.

6.0 Which Software Are Required for the Class?

Do I have to Purchase Any Software / License?

Throughout our AI / DS classes, only open-source or free software are used (with

exceptions of Microsoft Excel). The following are the list of software we would use in

the class (NB: You don’t have to have them installed before first class).

Software to be used in the class

S/No Software required (With version)

Description Which part of class will it be used? Download link / Installation Tutorial 1 Microsoft Excel A Microsoft office application for data analysis Introducing Data Analysis in Excel

(**If you have up-to-date Ms-Office running on your computer, you will be fine)

2. MySQL MySQL is an open-source relational database management system. Fundamentals of SQL for Data Analysis Download link: https://dev.mysql.com/downloads/mysql/

Installation Tutorial (YouTube Videos):

Mac: https://youtu.be/UcpHkYfWarM Window: https://youtu.be/kEnD_KN7P-k

3 Anaconda Python code

development environment that includes many integrated IDEs such as Jupyter Notebook and various python data science packages, such as Numpy, Scipy, Scikit-Learn, Pandas, and much more. Python for Data Analysis / Machine Learning / Deep Learning Download link:

https://www.anaconda.com/products/individual

Installation guide:

https://docs.anaconda.com/anaconda/install/

4 Tableau Desktop

A tool for data

Visualization Data Visualization We would discuss 14-day trial version installation before class

5 Power BI A tool for data visualization by Microsoft

Data

Visualization Win:

_{us/downloads/}

https://powerbi.microsoft.com/en-6 Tensorflow Google's

mathematics package for deep learning (Only for track 2).

Deep

Learning Installation to be shared later with Track 2 participants

7 Keras A deep learning framework built on top of TensorFlow Deep

7.0 Course Curriculum

TRACK 1 Course Curriculum

• Course Introduction and Setup

Module 02 - Introduction to Data Analytics

• Learning Objectives

• What is Data Analysis and What is the Value to Business

• Basics domain knowledge and conceptual understanding e.g, what Data Analysis is, Business Intelligence, Data Mining, Machine Learning, Data Science, Deep Learning, and Artificial Intelligence

• Drivers of Emerging growing interest in Analytics in Organizations • Types of Data Analytics

• From Data to Insights: Data Analytics Process Workflow • Data Analytics Techniques and Concepts

• Analytics Use Cases and Applications (Horizontal and Vertical Industries) • Stakeholders in an Analytics project and roles

• Planning an Analytics Projects – Key Steps, Tasks, Roles and Components • Hard and Soft Skills for Successful Career in Data Analytics

• Key Takeaways

Module 03 – Fundamental Statistics for Data Analysis

• Learning Objectives

• What is Statistics and why is it important in Data Analysis • Different Types of Statistics

• Different Types of Variables

• Statistical Measures (Central Tendency and Dispersion) and their significance • Data Distributions

• Hypothesis Testing • Type I and Type II error

• Forecasting and Time Series Analysis • Key Takeaways

Module 04 – Introduction Data Analysis in Excel

• Learning Objectives

• Introduction to Excel Spreadsheet • Reading and manipulating data;

• Basic excel data manipulation operations and functions, tips and tricks.

• Spreadsheet functions to organize data such as IF, nested IF, VLOOKUP and HLOOKUP functions) • Data Filtering, Pivot Tables, and Charts

• Advanced graphing and charting tips o Histogram, Line, etc • Key Takeaways

Module 05 – Fundamentals of SQL for Data Analysis

• Learning Objectives • What is SQL and RDBMS?

• SQL and modern languages such as HiveQL, SparkSQL • RDBMS Concepts fundamentals

o Tables and Data Types o Primary / Secondary Keys

• Basic SQL Commands to manipulate and retrieve data o SELECT Statement

o WHERE clause o UPDATE Statement

o JOINS (“left”, “right”, “outer”, “full”) o Groupings (GROUP BY)

o Aggregation Functions (e.g. AVG, MAX, SUM, COUNT) o Subquery

o Conditions with CASE (when/then/else/end) operator in SQL o LIKE and more complicated conditions

o Indexes o Wildcards • Creating Reusable Queries • Key Takeaways

Module 06 – Python for Analysis

• Learning Objectives

• Why Python for Data Analysis

• Introduction to Jupyter Notebook and Anaconda Environment • Basics of Python Programming Concepts for a Data Analyst

o The Python Interpreter o Modules o Functions o Data Structure: § Lists § Tuples § Dictionaries § Sets o Control Flow o Sorting o List Comprehensions o Iterators o Regular Expressions o Classes and OOP

o Zip and Argument Unpacking

• Mathematical Operations on Array using NumPy o What is NumPy?

o NumPy vs List o Array Creation

o Basics Operations on Array o Universal Function (Ufuncs) o Aggregation Functions o Broadcasting

o Fancy Indexing • Data Manipulation with Pandas

o Introduction and Motivation for Pandas o Pandas Data Structures

o Essential Functionalities

o Getting Data into and out of Pandas (OracleDB, SAS, SQLServer, Excel CSV, etc) o Pandas Object Creation (Series and DataFrame)

o Viewing Data

o Column and Row Selection (by label and position) o Exploratory Data Analysis

o Dealing with Missing Data

o Summarizing and Computing Descriptive Statistics o Data Wrangling

§ Merge/Join (Outer, Left, Right, Inner) § Object Concatenation

o Aggregation Grouping o Reshaping and Pivoting

§ Stacking and unstacking § Pivot Tables

o Working with Time Series

§ Dates and Times in Python

§ Pandas Time Series: indexing by Time § Pandas Time Series Data Structures § Frequencies and Offsets

§ Resampling, Shifting, and Windowing • Data Visualization with Matplotlib and Seaborn

o Introduction to Matplotlib and Seaborn o Line Plots

o Scatter Plots

o Density and Contour plots o Customizing Plots

o Visualization with Seaborn • Key Takeaways

Module 07 – Storytelling and Data Visualization (PowerBI / Tableau)

• Learning Objectives

• The Art of Storytelling and Best Practices for Telling Great Data Stories • Data Visualization Tools (Strength / Weaknesses of Each)

• Overview of Tableau / PowerBI Desktop Interface

• Foundation Concepts of Visualization in Tableau / PowerBI • Formatting Visualizations

• Creating Interactive Dashboards and Best Practices • Data Formatting for Tableau/PowerBI

• Key Takeaways

--- End of Track 1 --- Note: Track 2 = Track 1 + Track 2 Courses

TRACK 2 Course Curriculum

• Track 2 Content Introduction

Module 09 – The Machine Learning (ML) Landscape

• Learning Objectives

• What is Machine Learning and Why Machine Learning?

• Relationship Between Artificial Intelligence, Machine Learning, Deep Learning and Data Science • Examples of AI, ML, DL Applications – Real Life Use-Cases

• How Machines Learn o Supervised Learning o Unsupervised Learning o Reinforcement Learning • Main Challenges of ML

• Essential Python Libraries and Tools for Machine Learning and Deep Learning

Module 10 – Machine Learning Project Steps

• Look at the Big Picture and Frame the Problem • Getting the Data

• Explore and Visualize the Data to gain Insight • Prepare the Data for ML Algorithms

o Data Cleaning (Outliers, Missing Data) o Handling Text and Categorical Variables o Feature Selection

o Feature Transformation • Select and Train a Model

o Use of Cross-Validation • Fine-tune your model

• Generate Report or Launch your model in production

Module 10 - Supervised Learning Algorithm: Classification Algorithm

• Learning Objectives

• Types of Classification Algorithms • Training a Classifier

• Classification Algorithms: o K-Nearest Neighbours o Decision Trees

§ Building Decision Trees o Logistic Regression

§ Logistic regression vs Linear regression § Logistic Regression Training13m o Support Vector Machine8m

• Performance Measures

o Measuring Accuracy Using Cross-Validation o Confusion Matrix

o Precision and Recall o Precision/Recall trade-off o The ROC Curve

• Error Analysis

Module 11 - Supervised Learning Algorithm: Regression

• Learning Objectives • Linear Regression • Gradient Descent • Polynomial Regression

• Learning Curves

• Regularized Linear Models o Ridge Regression o Lasso Regression o Elastic Net o Early Stopping • Logistic Regression o Estimating probabilities o Training and Cost Function o Decision boundaries • Decision Trees

o Decision tree algorithms

o Evaluating performance of decision trees

Module 12 - Unsupervised Learning Algorithms:

• Learning Objectives

• Segmentation and Clustering Concepts • Clustering Algorithms

• Optimizing the number of Clusters • Evaluating Clustering performance

• Applying the k-means algorithm to clustering • Hierarchical Clustering

o Hierarchical Clustering: Example • Segmentation with Decision Trees

Module 13 – Text Analysis (Natural Language Processing)

• Learning Objectives • Overview of Text Analysis • Significance of Text Analysis

• Examples of Real-Life Applications of Text Analysis • Natural Language Toolkit Library

• Text Extraction and Preprocessing: Tokenization • Text Extraction and Preprocessing: N-grams

• Text Extraction and Preprocessing: Stemming • Text Extraction and Preprocessing: Lemmatization • Text Extraction and Preprocessing: POS Tagging

• Text Extraction and Preprocessing: Named Entity Recognition • NLP Process Workflow

• Structuring Sentences: Syntax • Rendering Syntax Trees

• Structuring Sentences: Chunking and Chunk Parsing • NP and VP Chunk and Parser

• Structuring Sentences: Chinking • Context-Free Grammar (CFG) • Key Takeaways

Module 14 – Computer Vision Basics

• Learning Objectives

• Overview of Computer Vision and Image Analysis • Examples of Real-Life Applications of Text Analysis • Overview of OpenCV toolkit

• Handling Images and Video Files • Reading and Writing Images • Capturing and Saving Videos

• Processing and Enhancing Images (Filtering, Sharpening, Denoising etc)

• Procession Colors (Color Spaces, Color-space-based segmentation, Color transfer) • Performing Feature Detection

• Detecting Specific Objects such as faces, eyes, cars, in videos or images

• Analyzing video (estimating the motion in it, subtract background, and track objects in it) • Key Takeaways

Module 15 – Introduction to Deep Learning

• Learning Objectives • What is Deep Learning • The Neuron

• The Activation Function • How do Neural Network Work? • How do Neural Network Learn?

• Backpropagation

• Step-by-Step how to build an Artificial Neural Network • Key Takeaways