DATA SCIENCE TRAINING IN LAGOS NIGERIA
DATA SCIENCE COURSE OVERVIEW
Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data - both structured and unstructured in order to make predictions.
Why we use Python for Data Science?
Python provides a more general approach to data science. Python is better for data
manipulation and repeated tasks. Python is a general-use high-level programming
language that bills itself as powerful, fast, friendly, open, and easy to learn.
The course is aimed at helping participants to have
- Practical data analysis skills that can be applied to practical problems.
- Fundamental knowledge of concepts underlying data science projects and processes.
- Practical skills needed in modern-day data analytics.
- Hands-on experience with real-world data analysis.
- Applied experience with data science software, programming, applications, and processes
Having a background in mathematical and statistics is helpful.
DATA SCIENCE COURSE OUTLINE (BEGINNERS)
Module 1: Introduction to data science
- Data Science Overview
- Fundamentals of Statistics
- Information visualization
- Big data
Module 2: Data collection and Data wrangling
- Sources of data and Data munging
- Data preparation and Pruning
- Data exploration
Module 3: Python for data science
- Setting up the programming environment
- Understanding the tools and libraries
- Loops and decisions
- Hands-on exercises
Module 4: Data visualization
- Techniques and processes
- Reporting and presentation
- Data manipulation
- Data science and Ethical issues
Machine learning fundamentals
Certificate of completion
INTERMEDIATE DATA SCIENCE COURSE OUTLINE
Overview of R and Python for data science
Data Visualization with Python (Matplotlib, Seaborn)
- Introduction to Matpotlib
- Colours, Markers, and line styles
- Customization of Matplotlib
- Plotting with Pandas
- Barplots, Histograms plots, and Density Plots
- Introduction to Seaborn and Style Management
- Plotting with Categorical Data
- Visualizing Linear Relationships
- Introduction to Statistics
- Categories of Statistics
- Data Collection
- Population and Sample
- Statistical Analysis Process
- Data Distributions
- Mean, Median, Mode
- Variance and Standard Deviation
- Covariance and correlation
- Hypothesis Testing
Introduction to Probability
- Estimating Probability
- Addition Rule
- Bayes Theorem
- Introduction to Hypothesis Testing
- Statistical Significance
- Hypothesis Testing – P-Value
- Hypothesis Testing – Pearson Correlation
Machine Learning Models
- Machine Learning Model?
- Types of Machine Learning
- Linear Regression
- Functions and Gradient Descent
- Polynomial Regression
- Logistic Regression
- Support Vector Machines (SVMs)
• Neural Networks Explained
• Forward Propagation
• Activation Functions
• Loss Functions
• Backpropagation and
• Gradient Descent
• Backpropagation & Learning Rates
• Regularization, Overfitting, Generalization and Test Datasets
• Epochs, Iterations, and Batch Sizes
• Measuring Performance and the
• Confusion Matrix
Introduction to Natural Language Processing (NLP)
- Introduction to Natural Language Processing
- Modeling Language – The Bag of Words Model
- Normalization, Stop Word Removal, Lemmatizing/Stemming
- TF-IDF Vectorizer (Term Frequency — Inverse Document Frequency)
- Word2Vec - Efficient Estimation of Word Representations in Vector Space
Artificial Neural Networks In Python
- Introduction to Neural Networks
- Single-layer neural network
- Multiple layer Neural network
- Backpropagation Algorithm
- Neural Networks Implementation in Python
- Introduction to Big Data
- Challenges in Big Data
- Hadoop, MapReduce, and Spark
- Introduction to PySpark
- RDDs, Transformations, Actions, Lineage Graphs & Jobs
- Working with Database
- Data Visualization and storytelling
Certificate of completion
Intermediate case studies
Predicting the US 2020 Election
Predicting Diabetes Cases
Predicting the World Cup Winner(Soccer/Football)
Covid-19 Data Analysis
Analyzing Olympic Winners
Streaming Services - Movie Analysis
Pizza Restaurant Data Analysis
Supply Chain Data Analysis
Africa Economic Crisis Data Analysis
Predicting Which Employees May Quit
Figuring Out Which Customers May Leave
Predicting Insurance Premiums
Predicting Airbnb Prices
Detecting Credit Card Fraud
Analyzing Conversion Rates in Marketing Campaigns
Predicting Advertising Engagement
Product Sales Analysis
Determining Your Most Valuable Customers
Build a Product Recommendation System
Movie Recommendation System - LiteFM
Deep Learning Recommendation System
Stock Trading using Reinforcement Learning
Detecting Sentiment in Tweets
Spam or Ham Detection
Explore Data with PySpark and Titanic Survival Prediction
Classification using PySpark
- Job recommendation
- Internship opportunity
- Access to our support community
BENEFITS OF LEARNING DATA SCIENCE
The various benefits of Data Science are as follows:
1. Data Science is greatly in demand.
Prospective job seekers have numerous opportunities. It is the fastest-growing job on Linkedin and is predicted to create 11.5 million jobs by 2026. This makes Data Science a highly employable job sector.
2. Abundance of Positions
There are very few people who have the required skill-set to become a complete Data Scientist. This makes Data Science less saturated as compared with other IT sectors. Therefore, Data Science is a vastly abundant field and has a lot of opportunities. The field of Data Science is high in demand but low in supply of Data Scientists.
3. A Highly Paid Career
Data Science is one of the most highly paid jobs. According to Glassdoor, Data scientists make an average of $116,100 per year. This makes Data Science a highly lucrative career option.
4. Data Science is Versatile
There are numerous applications of Data Science. It is widely used in health-care, banking, consultancy services, and e-commerce industries. Data Science is a very versatile field. Therefore, you will have the opportunity to work in various fields.
5. Data Science Makes Data Better
Companies require skilled Data Scientists to process and analyze their data. They not only analyze the data but also improve its quality. Therefore, Data Science deals with enriching data and making it better for their company.
6. Data Scientists are Highly Prestigious
Data Scientists allow companies to make smarter business decisions. Companies rely on Data Scientists and use their expertise to provide better results to their clients. This gives Data Scientists an important position in the company.
7. No More Boring Tasks
Data Science has helped various industries to automate redundant tasks. Companies are using historical data to train machines in order to perform repetitive tasks. This has simplified the arduous jobs undertaken by humans before.
8. Data Science Makes Products Smarter
Data Science involves the usage of Machine Learning which has enabled industries to create better products tailored specifically for customer experiences. For example, Recommendation Systems used by e-commerce websites provide personalized insights to users based on their historical purchases. This has enabled computers to understand human behaviour and make data-driven decisions.
9. Data Science can Save Lives
Healthcare sector has been greatly improved because of Data Science. With the advent of machine learning, it has been made easier to detect early-stage tumours. Also, many other health-care industries are using Data Science to help their clients.
10. Data Science Can Make You A Better Person
Data Science will not only give you a great career but will also help you in personal growth. You will be able to have a problem-solving attitude. Since many Data Science roles bridge IT and Management, you will be able to enjoy the best of both worlds.
6 weeks (Saturdays)
2 weeks (Weekdays)
Time: 10am to 2pm
Venue: Tech365, Nikky Africana, 1st Floor, 70c, Allen Avenue, Opp UBA, First Bank Bus Stop, Allen, Ikeja