Time to Complete 11 Months at 30-40 hrs/week, Online
Course Difficulty Advanced
Your Competitive Edge in the Global Job Market
The world runs on data. The need for data scientists who can expertly analyze data, extract critical insights, and effectively present the findings have never been greater.
Our program will teach you crucial tools and techniques used to analyze visual and statistical data, create models, and communicate results to inform data-driven decisions. It is designed to teach you the most in-demand data science and machine learning skills to place you at the cutting edge of the digital economy.
You’ll use Python programming fundamentals to build data models, build dashboards to tell compelling data-driven stories, work with SQL to analyze databases – the possibilities are endless.
Your future starts today. No prior experience necessary.
Qualify for In-Demand jobs in Data Science
The employment rate for data scientists is projected to grow by 36% over the next decade.
$90,000
Median entry-level salary in data science in the US
204,000+
Open jobs in data science on LinkedIn
Why This Program Is Right For You
You are new to data science or come from a tech adjacent industry and you’re looking to future-proof your skillset.
You are looking for a program that can work around your existing schedule. You are able to dedicate up to 35-hours per week to your course.
You are inspired by Big Data and have a strong desire to learn how to use that data to solve real-world problems or you are looking to take your first steps into the world of machine learning and AI.
Dive Into Data Science
This fully online program teaches you the skills you need to find an entry-level role in data science, as well as the fundamentals to continue your development in machine learning and data engineering, securing your place in the digital economy. Tech is constantly evolving – are you?
Build on your career as a:
- Data Scientist
- Data Engineer
- Big Data Architect
- Machine Learning Engineer
- Artificial Intelligence Engineer
- Data Management Specialist
- BI Analyst
- Quantitative Analyst
Program Curriculum
What You'll Learn
- Understanding common data terminology and build dashboards to tell compelling stories with your data.
- The fundamentals of Python programming.
- Work with SQL to use databases for storing, reading, and updating data sets.
- Prepare data for modeling, build regression prediction models with Python, and fine-tune them for best results.
Skills You'll Master
- Google Sheets
- Python
- Jupyter Notebooks
- MySQL
- Power BI
Course Duration & Learning Modalities
11
months
30-40 hours per week
-
15%
Live Learning
6 hours / week
-
10%
Peer Learning
4 hours / week
-
70%
Doing Projects
28 hours / week
-
5%
Taking Quizzes
2 hours / week
Explore 101
Orientation
- Setting up your learning environment
- ALX-Global teaching philosophy and educational support framework
- Complete onboarding assessments and recommended reading
Introduction to data and data analytics
- Setting up your learning environment
- ExploreAI teaching philosophy and educational support framework
- Troubleshooting at ExploreAI Academy
Problem-solving
- Setting up your learning environment
- ExploreAI teaching philosophy and educational support framework
- Troubleshooting at ExploreAI Academy
Programmatic thinking
- Setting up your learning environment
- ExploreAI teaching philosophy and educational support framework
- Troubleshooting at ExploreAI Academy
Preparing data
Introduction to spreadsheets
- Working with spreadsheets
- Data types and formatting
- Introduction to visualisation
Data manipulation
- Cleaning and analyzing spreadsheet data
- Working with various data types
- Finding and fixing data anomalies
Introduction to statistics
- Summarizing data using descriptive statistics
- Measures of central tendency and spread
- Samples and distributions
Introduction to data modeling
- Basic spreadsheet functions and conditionals
- Identifying patterns and the line of best fit
- Testing assumptions and model accuracy
SQL
Introduction to SQL
- Working with databases
Basic SQL data types and calculations
Aggregating, sorting, and grouping data
Relational database design
- SQL schemas and entity relationships
- Table normalisation, primary and foreign keys
- Common table expressions and views
SQL in practice
- Set theory and SQL joins
- Nested and subqueries
- Improving query performance
Data manipulation
- Cleaning and analysing data
- Working with numeric, time, and string data types
- Data transformations and anomalies
Data Visualization and Storytelling
Data in Power BI
- Loading and linking datasets in Power BI
- Cleaning data and creating calculated columns and measures using DAX
- Reports, data, and relationship views
Visuals in Power BI
- Numeric visuals – cards, tables
- Graphic visuals – line chart, bar chart, pie chart, column chart,
treemap - Using slicers and custom visuals
Dashboards
- Planning, designing, and prototyping
- Working with various charts
- Working with filters
Visual storytelling
- Telling a story with visuals
- When to use which visuals
- Presentation best-practice
Python
Python programming basics
- Working in a Notebook environment
- Pseudo code and debugging concepts
- Working with primitive data types – variables, strings, integers, floating points, booleans
Functions and control flow
- Creating and working with functions
- Conditional statements
- For loops and while loops
Data structures
- Lists, tuples, sets, and dictionaries
- Working with Data Frames
- Plots and graphs
Exploratory data analysis
- Statistical measures, probabilities, and hypotheses
- Algorithms and algorithmic complexity
- Advanced interactive visual analysis
Regression
Steps to build a model
- Statistical learning, univariate and multivariate analysis
- Training models, making predictions, testing accuracy
- Variable significance and selection
Preparing data for modelling
- Defining or engineering features and labels
- Scaling, standardisation, and regularisation techniques
- Splitting data for training, testing, and validation
Algorithms for regression models
- K-nearest neighbours
- Decision trees and random forests
- Support vector machines
Model tuning
- Model performance metrics
- Bias and variance
- Hyperparameter tuning
Natural language processing and classification
An overview of natural language processing
- Removing punctuation and symbols
- Stopwords and regular expressions
- Tokenizing text
Analyzing text
- Lemmatization of words
- Bag of words
- Sentiment analysis
Basic classification
- Logistic regression and binary classification models
- Testing model output: confusion matrix, classification report
- Feature engineering and selection
Advanced classification
- Hyperparameters and model validation
- Dealing with imbalanced data and multi-class classification
- Neural networks and image classification
Unsupervised learning
Dimensionality reduction
- Principal component analysis
- Multidimensional scaling
- Interpreting nonlinear transformations and embeddings
Hard and hierarchical clustering
- What is clustering?
- K-means clustering
- Hierarchical clustering
Soft clustering
- Gaussian mixture models
- Linear discriminant analysis and text clustering
- Labelling data using cluster output
Recommender systems
- Measures of product similarity
- Content and collaborative-based filtering
- Evaluating a recommender system
AWS foundations
Cloud computing basics
- Introduction to cloud computing concepts
- Pros and cons of cloud computing
- Popular cloud service providers
Introduction to Amazon Web Services
- Overview of AWS services
- Networking and content delivery
- Economics and billing
Storage and compute resources
- Databases and object storage
- Virtual machines
- Serverless compute resources
Cloud best practice
- Security, identity, and compliance
- Cloud architecture framework
- Automatic scaling and monitoring
Explore 101
Orientation
- Setting up your learning environment
- ALX-Global teaching philosophy and educational support framework
- Complete onboarding assessments and recommended reading
Introduction to data and data analytics
- Setting up your learning environment
- ExploreAI teaching philosophy and educational support framework
- Troubleshooting at ExploreAI Academy
Problem-solving
- Setting up your learning environment
- ExploreAI teaching philosophy and educational support framework
- Troubleshooting at ExploreAI Academy
Programmatic thinking
- Setting up your learning environment
- ExploreAI teaching philosophy and educational support framework
- Troubleshooting at ExploreAI Academy
Preparing data
Introduction to spreadsheets
- Working with spreadsheets
- Data types and formatting
- Introduction to visualisation
Data manipulation
- Cleaning and analyzing spreadsheet data
- Working with various data types
- Finding and fixing data anomalies
Introduction to statistics
- Summarizing data using descriptive statistics
- Measures of central tendency and spread
- Samples and distributions
Introduction to data modeling
- Basic spreadsheet functions and conditionals
- Identifying patterns and the line of best fit
- Testing assumptions and model accuracy
SQL
Introduction to SQL
- Working with databases
Basic SQL data types and calculations
Aggregating, sorting, and grouping data
Relational database design
- SQL schemas and entity relationships
- Table normalisation, primary and foreign keys
- Common table expressions and views
SQL in practice
- Set theory and SQL joins
- Nested and subqueries
- Improving query performance
Data manipulation
- Cleaning and analysing data
- Working with numeric, time, and string data types
- Data transformations and anomalies
Data Visualization and Storytelling
Data in Power BI
- Loading and linking datasets in Power BI
- Cleaning data and creating calculated columns and measures using DAX
- Reports, data, and relationship views
Visuals in Power BI
- Numeric visuals – cards, tables
- Graphic visuals – line chart, bar chart, pie chart, column chart,
treemap - Using slicers and custom visuals
Dashboards
- Planning, designing, and prototyping
- Working with various charts
- Working with filters
Visual storytelling
- Telling a story with visuals
- When to use which visuals
- Presentation best-practice
Python
Python programming basics
- Working in a Notebook environment
- Pseudo code and debugging concepts
- Working with primitive data types – variables, strings, integers, floating points, booleans
Functions and control flow
- Creating and working with functions
- Conditional statements
- For loops and while loops
Data structures
- Lists, tuples, sets, and dictionaries
- Working with Data Frames
- Plots and graphs
Exploratory data analysis
- Statistical measures, probabilities, and hypotheses
- Algorithms and algorithmic complexity
- Advanced interactive visual analysis
Regression
Steps to build a model
- Statistical learning, univariate and multivariate analysis
- Training models, making predictions, testing accuracy
- Variable significance and selection
Preparing data for modelling
- Defining or engineering features and labels
- Scaling, standardisation, and regularisation techniques
- Splitting data for training, testing, and validation
Algorithms for regression models
- K-nearest neighbours
- Decision trees and random forests
- Support vector machines
Model tuning
- Model performance metrics
- Bias and variance
- Hyperparameter tuning
Natural language processing and classification
An overview of natural language processing
- Removing punctuation and symbols
- Stopwords and regular expressions
- Tokenizing text
Analyzing text
- Lemmatization of words
- Bag of words
- Sentiment analysis
Basic classification
- Logistic regression and binary classification models
- Testing model output: confusion matrix, classification report
- Feature engineering and selection
Advanced classification
- Hyperparameters and model validation
- Dealing with imbalanced data and multi-class classification
- Neural networks and image classification
Unsupervised learning
Dimensionality reduction
- Principal component analysis
- Multidimensional scaling
- Interpreting nonlinear transformations and embeddings
Hard and hierarchical clustering
- What is clustering?
- K-means clustering
- Hierarchical clustering
Soft clustering
- Gaussian mixture models
- Linear discriminant analysis and text clustering
- Labelling data using cluster output
Recommender systems
- Measures of product similarity
- Content and collaborative-based filtering
- Evaluating a recommender system
AWS foundations
Cloud computing basics
- Introduction to cloud computing concepts
- Pros and cons of cloud computing
- Popular cloud service providers
Introduction to Amazon Web Services
- Overview of AWS services
- Networking and content delivery
- Economics and billing
Storage and compute resources
- Databases and object storage
- Virtual machines
- Serverless compute resources
Cloud best practice
- Security, identity, and compliance
- Cloud architecture framework
- Automatic scaling and monitoring
How You’ll learn
Work through downloadable content and online instructional material.
Interact with your peers and facilitators through the ALX Global forum.
Enjoy a wide range of interactive content, including video lectures and walk-throughs..
Apply what you learn each week in quizzes and ongoing project submissions, sharpening your ability to solve real-world problems.
Investigate real-world case studies.
Fees & Financing
-
Full Fees
US $3950.00
-
Pay Up Front - SAVE 20%
US $3160.00
One-time payment, 20% Off
-
Pay-As-You-Go
US $360/mo
Pay US $360 up front and then split the remaining balance over 10 installments
Frequently Asked Questions
Data Science is for those looking to use data to build solutions and solve problems. It is suitable for learners starting their career journey, those who might be looking to improve their skills to grow in their existing careers or to transition to a role in tech. During your program you will tackle in-depth concepts of Data Analytics as well as the fundamentals of machine learning and AI. The Data Science program provides a stepping stone to further specialized careers as Data Engineering and AI.
This program has no prerequisite requirements or prior experience needed in web development, coding, or tech. However, to be successful we do require you to be able to dedicate up to 20 hours a week for the duration of the program. You will be required to have access to a steady internet connections and be proficient in written and spoken English.
The ALX Data Science program provides you with sought-after skills such as data exploration, insight building, and improving and communicating models from a raw and unstructured dataset. Data Science is a high-growth emerging profession with 204,000+ Open jobs on LinkedIn and projected growth in employment rate of 36%.
Yes you will, successful graduates will receive a certificate of completion.
You can sign up to our programs directly from the ALX-Global website. Click the ‘Sign Up Now’ button to go straight the program sign up page.
ALX Global accepts a wide variety of payment methods, including Google and Apple Pay, Paypal and all major credit cards.