Job-ready in 11 months

Data Science – Early Registration

  • 11 months completion time
  • 100% remote, online learning
  • Hands-on, project-based training
  • No pre-requisite skills required

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 $3960.00

    Retail cost

  • Pay Up Front - SAVE 20%

    US $3160.00

    One-time payment, 20% Off

  • Pay-As-You-Go

    US $360.00/mo

    Pay US $360 up front and then split the remaining balance over 10 installments

See Payment & Cancellation Policy

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 prerequisites or prior experience required in web development, coding, or tech required. However, to be successful we do require you to be able to dedicate up to 20 hours a week 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 ‘Reserve your Seat’ 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.

Pay-As-You-Learn Pay In Full

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$3,960, now $3,168

$2880 first month

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$2,880.00 May 19, 2024 4:21 am
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$3,960, now $2,772
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$2,673 balance in month two

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