DATA SCIENCE COURSE IN DAKAR, SENEGAL

MODULE 1: DATA SCIENCE ESSENTIALS 

 • Introduction to Data Science
 • Evolution of Data Science
 • Big Data Vs Data Science
 • Data Science Terminologies
 • Data Science vs AI/Machine Learning
 • Data Science vs Analytics

MODULE 2: DATA SCIENCE DEMO

 • Business Requirement: Use Case
 • Data Preparation
 • Machine learning Model building
 • Prediction with ML model
 • Delivering Business Value.

MODULE 3: ANALYTICS CLASSIFICATION 

 • Types of Analytics
 • Descriptive Analytics
 • Diagnostic Analytics
 • Predictive Analytics
 • Prescriptive Analytics
 • EDA and insight gathering demo in Tableau

MODULE 4: DATA SCIENCE AND RELATED FIELDS

 • Introduction to AI
 • Introduction to Computer Vision
 • Introduction to Natural Language Processing
 • Introduction to Reinforcement Learning
 • Introduction to GAN
 • Introduction to Generative Passive Models

MODULE 5: DATA SCIENCE ROLES & WORKFLOW

 • Data Science Project workflow
 • Roles: Data Engineer, Data Scientist, ML Engineer and MLOps Engineer
 • Data Science Project stages.

MODULE 6: MACHINE LEARNING INTRODUCTION

 • What Is ML? ML Vs AI
 • ML Workflow, Popular ML Algorithms
 • Clustering, Classification And Regression
 • Supervised Vs Unsupervised

MODULE 7: DATA SCIENCE INDUSTRY APPLICATIONS

 • Data Science in Finance and Banking
 • Data Science in Retail
 • Data Science in Health Care
 • Data Science in Logistics and Supply Chain
 • Data Science in Technology Industry
 • Data Science in Manufacturing
 • Data Science in Agriculture

MODULE 1: PYTHON BASICS 

 • Introduction of python
 • Installation of Python and IDE
 • Python Variables
 • Python basic data types
 • Number & Booleans, strings
 • Arithmetic Operators
 • Comparison Operators
 • Assignment Operators

MODULE 2: PYTHON CONTROL STATEMENTS 

 • IF Conditional statement
 • IF-ELSE
 • NESTED IF
 • Python Loops basics
 • WHILE Statement
 • FOR statements
 • BREAK and CONTINUE statements

MODULE 3: PYTHON DATA STRUCTURES 

 • Basic data structure in python
 • Basics of List
 • List: Object, methods
 • Tuple: Object, methods
 • Sets: Object, methods
 • Dictionary: Object, methods

MODULE 4: PYTHON FUNCTIONS 

 • Functions basics
 • Function Parameter passing
 • Lambda functions
 • Map, reduce, filter functions

MODULE 1: OVERVIEW OF STATISTICS 

 • Introduction to Statistics
 • Descriptive And Inferential Statistics
 • Basic Terms Of Statistics
 • Types Of Data

MODULE 2: HARNESSING DATA 

 • Random Sampling
 • Sampling With Replacement And Without Replacement
 • Cochran's Minimum Sample Size
 • Types of Sampling
 • Simple Random Sampling
 • Stratified Random Sampling
 • Cluster Random Sampling
 • Systematic Random Sampling
 • Multi stage Sampling
 • Sampling Error
 • Methods Of Collecting Data

MODULE 3: EXPLORATORY DATA ANALYSIS 

 • Exploratory Data Analysis Introduction
 • Measures Of Central Tendencies: Mean,Median And Mode
 • Measures Of Central Tendencies: Range, Variance And Standard Deviation
 • Data Distribution Plot: Histogram
 • Normal Distribution & Properties
 • Z Value / Standard Value
 • Empirical Rule and Outliers
 • Central Limit Theorem
 • Normality Testing
 • Skewness & Kurtosis
 • Measures Of Distance: Euclidean, Manhattan And Minkowski Distance
 • Covariance & Correlation

MODULE 4: HYPOTHESIS TESTING 

 • Hypothesis Testing Introduction
 • P- Value, Critical Region
 • Types of Hypothesis Testing
 • Hypothesis Testing Errors : Type I And Type II
 • Two Sample Independent T-test
 • Two Sample Relation T-test
 • One Way Anova Test
 • Application of Hypothesis testing

MODULE 1: MACHINE LEARNING INTRODUCTION 

 • What Is ML? ML Vs AI
 • Clustering, Classification And Regression
 • Supervised Vs Unsupervised

MODULE 2:  PYTHON NUMPY  PACKAGE 

 • Introduction to Numpy Package
 • Array as Data Structure
 • Core Numpy functions
 • Matrix Operations, Broadcasting in Arrays

MODULE 3:  PYTHON PANDAS PACKAGE 

 • Introduction to Pandas package
 • Series in Pandas
 • Data Frame in Pandas
 • File Reading in Pandas
 • Data munging with Pandas

MODULE 4: VISUALIZATION WITH PYTHON - Matplotlib

 • Visualization Packages (Matplotlib)
 • Components Of A Plot, Sub-Plots
 • Basic Plots: Line, Bar, Pie, Scatter

MODULE 5: PYTHON VISUALIZATION PACKAGE - SEABORN

 • Seaborn: Basic Plot
 • Advanced Python Data Visualizations

MODULE 6: ML ALGO: LINEAR REGRESSSION

 • Introduction to Linear Regression
 • How it works: Regression and Best Fit Line
 • Modeling and Evaluation in Python

MODULE 7: ML ALGO: LOGISTIC REGRESSION

 • Introduction to Logistic Regression
 • How it works: Classification & Sigmoid Curve
 • Modeling and Evaluation in Python

MODULE 8: ML ALGO: K MEANS CLUSTERING

 • Understanding Clustering (Unsupervised)
 • K Means Algorithm
 • How it works : K Means theory
 • Modeling in Python

MODULE 9: ML ALGO: KNN

 • Introduction to KNN
 • How It Works: Nearest Neighbor Concept
 • Modeling and Evaluation in Python

MODULE 1: FEATURE ENGINEERING 

 • Introduction to Feature Engineering
 • Feature Engineering Techniques: Encoding, Scaling, Data Transformation
 • Handling Missing values, handling outliers
 • Creation of Pipeline
 • Use case for feature engineering

MODULE 2: ML ALGO: SUPPORT VECTOR MACHINE (SVM)

 • Introduction to SVM
 • How It Works: SVM Concept, Kernel Trick
 • Modeling and Evaluation of SVM in Python

MODULE 3: PRINCIPAL COMPONENT ANALYSIS (PCA)

 • Building Blocks Of PCA
 • How it works: Finding Principal Components
 • Modeling PCA in Python

MODULE 4:  ML ALGO: DECISION TREE 

 • Introduction to Decision Tree & Random Forest
 • How it works
 • Modeling and Evaluation in Python

MODULE 5: ENSEMBLE TECHNIQUES - BAGGING 

 • Introduction to Ensemble technique 
 • Bagging and How it works
 • Modeling and Evaluation in Python

MODULE 6: ML ALGO: NAÏVE BAYES

 • Introduction to Naive Bayes
 • How it works: Bayes' Theorem
 • Naive Bayes For Text Classification
 • Modeling and Evaluation in Python

MODULE 7: GRADIENT BOOSTING, XGBOOST

 • Introduction to Boosting and XGBoost
 • How it works?
 • Modeling and Evaluation of in Python

MODULE 1: TIME SERIES FORECASTING - ARIMA 

 • What is Time Series?
 • Trend, Seasonality, cyclical and random
 • Stationarity of Time Series
 • Autoregressive Model (AR)
 • Moving Average Model (MA)
 • ARIMA Model
 • Autocorrelation and AIC
 • Time Series Analysis in Python 

MODULE 2: SENTIMENT ANALYSIS 

 • Introduction to Sentiment Analysis
 • NLTK Package
 • Case study: Sentiment Analysis on Movie Reviews

MODULE 3: REGULAR EXPRESSIONS WITH PYTHON 

 • Regex Introduction
 • Regex codes
 • Text extraction with Python Regex

MODULE 4:  ML MODEL DEPLOYMENT WITH FLASK 

 • Introduction to Flask
 • URL and App routing
 • Flask application – ML Model deployment

MODULE 5: ADVANCED DATA ANALYSIS WITH MS EXCEL

 • MS Excel core Functions
 • Advanced Functions (VLOOKUP, INDIRECT..)
 • Linear Regression with EXCEL
 • Data Table
 • Goal Seek Analysis
 • Pivot Table
 • Solving Data Equation with EXCEL

MODULE 6:  AWS CLOUD FOR DATA SCIENCE

 • Introduction of cloud
 • Difference between GCC, Azure, AWS
 • AWS Service ( EC2 instance)

MODULE 7: AZURE FOR DATA SCIENCE

 • Introduction to AZURE ML studio
 • Data Pipeline
 • ML modeling with Azure

MODULE 8:  INTRODUCTION TO DEEP LEARNING

 • Introduction to Artificial Neural Network, Architecture
 • Artificial Neural Network in Python
 • Introduction to Convolutional Neural Network, Architecture
 • Convolutional Neural Network in Python

MODULE 1: DATABASE INTRODUCTION 

 • DATABASE Overview
 • Key concepts of database management
 • Relational Database Management System
 • CRUD operations

MODULE 2:  SQL BASICS

 • Introduction to Databases
 • Introduction to SQL
 • SQL Commands
 • MY SQL workbench installation

MODULE 3: DATA TYPES AND CONSTRAINTS 

 • Numeric, Character, date time data type
 • Primary key, Foreign key, Not null
 • Unique, Check, default, Auto increment

MODULE 4: DATABASES AND TABLES (MySQL) 

 • Create database
 • Delete database
 • Show and use databases
 • Create table, Rename table
 • Delete table, Delete table records
 • Create new table from existing data types
 • Insert into, Update records
 • Alter table

MODULE 5: SQL JOINS 

 • Inner Join, Outer Join
 • Left Join, Right Join
 • Self Join, Cross join
 • Windows function: Over, Partition, Rank

MODULE 6: SQL COMMANDS AND CLAUSES 

 • Select, Select distinct
 • Aliases, Where clause
 • Relational operators, Logical
 • Between, Order by, In
 • Like, Limit, null/not null, group by
 • Having, Sub queries

MODULE 7 : DOCUMENT DB/NO-SQL DB 

 • Introduction of Document DB
 • Document DB vs SQL DB
 • Popular Document DBs
 • MongoDB basics
 • Data format and Key methods

MODULE 1: GIT  INTRODUCTION 

 • Purpose of Version Control
 • Popular Version control tools
 • Git Distribution Version Control
 • Terminologies
 • Git Workflow
 • Git Architecture

MODULE 2: GIT REPOSITORY and GitHub 

 • Git Repo Introduction
 • Create New Repo with Init command
 • Git Essentials: Copy & User Setup
 • Mastering Git and GitHub

MODULE 3: COMMITS, PULL, FETCH AND PUSH 

 • Code Commits
 • Pull, Fetch and Conflicts resolution
 • Pushing to Remote Repo

MODULE 4: TAGGING, BRANCHING AND MERGING 

 • Organize code with branches
 • Checkout branch
 • Merge branches
 • Editing Commits
 • Commit command Amend flag
 • Git reset and revert

MODULE 5: GIT WITH GITHUB AND BITBUCKET

 • Creating GitHub Account
 • Local and Remote Repo
 • Collaborating with other developers

MODULE 1: BIG DATA INTRODUCTION 

 • Big Data Overview
 • Five Vs of Big Data
 • What is Big Data and Hadoop
 • Introduction to Hadoop
 • Components of Hadoop Ecosystem
 • Big Data Analytics Introduction

MODULE 2 : HDFS AND MAP REDUCE 

 • HDFS – Big Data Storage
 • Distributed Processing with Map Reduce
 • Mapping and reducing stages concepts
 • Key Terms: Output Format, Partitioners,
 • Combiners, Shuffle, and Sort

MODULE 3: PYSPARK FOUNDATION 

 • PySpark Introduction
 • Spark Configuration
 • Resilient distributed datasets (RDD)
 • Working with RDDs in PySpark
 • Aggregating Data with Pair RDDs

MODULE 4: SPARK SQL and HADOOP HIVE 

 • Introducing Spark SQL
 • Spark SQL vs Hadoop Hive

MODULE 1: TABLEAU FUNDAMENTALS 

 • Introduction to Business Intelligence & Introduction to Tableau
 • Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
 • Bar chart, Tree Map, Line Chart
 • Area chart, Combination Charts, Map
 • Dashboards creation, Quick Filters
 • Create Table Calculations
 • Create Calculated Fields
 • Create Custom Hierarchies

MODULE 2:  POWER-BI BASICS

 • Power BI Introduction 
 • Basics Visualizations
 • Dashboard Creation
 • Basic Data Cleaning
 • Basic DAX FUNCTION

MODULE 3 : DATA TRANSFORMATION TECHNIQUES 

 • Exploring Query Editor
 • Data Cleansing and Manipulation:
 • Creating Our Initial Project File
 • Connecting to Our Data Source
 • Editing Rows
 • Changing Data Types
 • Replacing Values

MODULE 4: CONNECTING TO VARIOUS DATA SOURCES 

• Connecting to a CSV File
 • Connecting to a Webpage
 • Extracting Characters
 • Splitting and Merging Columns
 • Creating Conditional Columns
 • Creating Columns from Examples
 • Create Data Model

Data Science entails extracting insights from extensive datasets through statistical analysis, machine learning, and data interpretation. It serves as a crucial field in transforming raw data into actionable knowledge, influencing decision-making across various sectors.

Eligibility for data science certification courses extends to individuals with backgrounds in mathematics, computer science, or related fields. However, a passion for problem-solving and data analysis is equally valuable.

The workings of Data Science involve collecting, processing, and interpreting data through statistical methods and machine learning algorithms. This process uncovers patterns and trends, facilitating informed decision-making and predictive modeling for businesses and organizations.

A career in Data Science typically requires a degree in computer science, mathematics, statistics, or a related field. Additionally, practical skills in programming, data analysis, and statistical modeling are essential for success.

In Dakar, a Data Scientist's career path can progress from entry-level analyst roles to senior positions like a machine learning engineer or Data Science Manager. Continuous learning and staying updated on industry trends are pivotal for career advancement.

To commence a Data Science Career in Dakar, establish a robust educational foundation, develop skills through courses and projects, create a compelling portfolio, and pursue internships or entry-level positions. Networking within the local data science community is key for seizing opportunities.

The premier data science course in Dakar is the Certified Data Scientist program, offering participants essential skills in statistical analysis, machine learning, and data interpretation. This comprehensive course ensures a thorough understanding of the field, enhancing employability in diverse data science roles.

Essential skills for a Data Scientist encompass proficiency in programming languages (e.g., Python, R), data analysis, machine learning, statistical modeling, and effective communication. Critical thinking, problem-solving, and domain knowledge further contribute to success in the field.

In Dakar, data scientists receive competitive salaries, mirroring the global landscape. Although specific figures may vary, Indeed indicates that data scientists in Dakar are well-compensated, aligning with the high standards observed in the United States. While the exact salary range is not specified, the reported average annual salary for a Data Scientist in the United States is $123,442, highlighting the lucrative nature of data science roles in Dakar.

To stay current in Data Science, engage in continuous learning through online courses, attend conferences, join forums, and apply knowledge in real projects, ensuring relevance in a rapidly evolving field.