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There are 4 Tracks for the Data Analyst to Data Science course (Data Analyst, Data Wrangler, Data Ops and Data Science). Each stage of the journey delivers 40-50 hours of courses + multimodal content and an additional 10-12 Practice Labs, Certification Pre/Assessments.

Data Analyst to Data Science

Data Science: Track 1: Data Analyst

Data Architecture Primer
Data Engineering Fundamentals

Python for Data Science
Python for Data Science: Introduction to NumPy for Multi-dimentional Data
Python for Data Science: Advanced Operations with NumPy Arrays
Python for Data Science: Introduction to Pandas
Python for Data Science: Manipulating and Analyzing Data in Pandas DataFrames

R for Data Science
R for Data Science: Data Structures
R for Data Science: Importing and Exporting Data
R for Data Science: Data Exploration
R for Data Science: Regression Methods
R for Data Science: Classification & Clustering

Data Science Statistics
Data Science Statistics: Simple Descriptive Statistics
Data Science Statistics: Common Approaches to Sampling Data
Data Science Statistics: Inferential Statistics

Accessing Data with Spark: An Introduction to Spark

Getting Started with Hadoop: Fundamentals & MapReduce
Getting Started with Hadoop: Developing a Basic MapReduce Application
Hadoop HDFS: Introduction
Hadoop HDFS: Introduction to the Shell
Hadoop HDFS: Working with Files
Hadoop HDFS: File Permissions

Data Silos, Lakes, & Streams
Data Silos, Lakes, & Streams: Introduction
Data Silos, Lakes, and Streams: Data Lakes on AWS
Data Silos, Lakes, & Streams: Sources, Visualizations, & ETL Operations

Data Analysis Application

Data Science: Track 2: Data Wrangler

Data Wrangling with Pandas
Data Wrangling with Pandas: Working with Series & DataFrames
Data Wrangling with Pandas: Visualizations and Time-Series Data
Data Wrangling with Pandas: Advanced Features

Data Wrangler 4: Cleaning Data in R

Data Tools
Data Tools: Technology Landscape & Tools for Data Management
Data Tools: Machine Learning & Deep Learning in the Cloud

Trifacta for Data Wrangling: Wrangling Data

MongoDB for Data Wrangling
MongoDB for Data Wrangling: Querying
MongoDB for Data Wrangling: Aggregation

Getting Started with Hive: Introduction
Getting Started with Hive: Loading and Querying Data
Getting Started with Hive: Viewing and Querying Complex Data
Getting Started with Hive: Optimizing Query Execution
Getting Started with Hive: Optimizing Query Executions with Partitioning
Getting Started with Hive: Bucketing & Window Functions

Getting Started with Hadoop: Filtering Data Using MapReduce
Getting Started with Hadoop: MapReduce Applications With Combiners
Getting Started with Hadoop: Advanced Operations Using MapReduce

Accessing Data with Spark: Data Analysis Using the Spark DataFrame API
Accessing Data with Spark: Data Analysis using Spark SQL

Data Lake
Data Lake: Framework & Design Implementation
Data Lake: Architectures & Data Management Principles

Building Data Pipelines

Data Architecture
Data Architecture - Deep Dive: Design & Implementation
Data Architecture - Deep Dive: Microservices & Serverless Computing

Data Science: Track 3: Data Ops

Deploying Data Tools: Data Science Tools

Delivering Dashboards
Delivering Dashboards: Management Patterns
Delivering Dashboards: Exploration & Analytics

Cloud Data Architecture: DevOps & Containerization

Compliance Issues and Strategies: Data Compliance

Implementing Governance Strategies

Data Access & Governance Policies
Data Access & Governance Policies: Data Access Oversight and IAM
Data Access & Governance Policies: Data Classification, Encryption, and Monitoring

Streaming Data Architectures
Streaming Data Architectures: An Introduction to Streaming Data
Streaming Data Architectures: Processing Streaming Data

Scalable Data Architectures
Scalable Data Architectures: Introduction
Scalable Data Architectures: Introduction to Amazon Redshift
Scalable Data Architectures: Working with Amazon Redshift & QuickSight

Data Sources
Data Sources: Integration
Data Sources: Implementing Edge on the Cloud

Data Ops 16: Securing Big Data Streams

Harnessing Data Volume & Velocity: Big Data to Smart Data

Data Rollbacks
Data Rollbacks: Transaction Rollbacks & Their Impact
Data Rollbacks: Transaction Management & Rollbacks in NoSQL

Data Science: Track 4: Data Science

Balancing the Four Vs of Data: The Four Vs of Data

Data Science 2: Data Driven Organizations

Raw Data to Insights
Raw Data to Insights: Data Ingestion & Statistical Analysis
Raw Data to Insights: Data Management & Decision Making

Tableau Desktop: Real Time Dashboards

Python for Data Science
Python for Data Science: Basic Data Visualization Using Seaborn
Python for Data Science: Advanced Data Visualization Using Seaborn

Data Science Statistics: Using Python to Compute & Visualize Statistics

R for Data Science: Data Visualization

Powering Recommendation Engines: Recommendation Engines

Data Insights, Anomalies, & Verification
Data Insights, Anomalies, & Verification: Handling Anomalies
Data Insights, Anomalies, & Verification: Machine Learning & Visualization Tools

Data Science
Data Science Statistics: Applied Inferential Statistics
Data Science 9: Data Research Techniques
Data Science 10: Data Research Exploration Techniques
Data Scientist 14: Data Research Statistical Approaches

Machine & Deep Learning Algorithms
Machine & Deep Learning Algorithms: Introduction
Machine & Deep Learning Algorithms: Regression & Clustering
Machine & Deep Learning Algorithms: Data Preperation in Pandas ML
Machine & Deep Learning Algorithms: Imbalanced Datasets Using Pandas ML

Creating Data APIs Using Node.js

Business and Leadership Skills for Data Science
Developing a Growth Mind-set
Developing Your Business Acumen
Big Data Interpretation
Using Strategic Thinking to Consider the Big Picture
Using Active Listening in the Workplace Situations
Choosing the Right Interpersonal Communication Method to Make Your Point
Enabling Business Process Improvement
Finding the Quality in Your Data
Six Sigma Measurement System Analysis
Confronting Your Assumptions
Motivation Your Employees
Capturing the Attention of Senior Executives

Productivity and Collaboration Skills for Data Science
Slack 2016: Private Messaging & Communication Tools
Confluence: Signing in & Navigating within Spaces
Confluence: Setting Up & Managing Spaces
Confluence: Working with Spaces
Confluence: Working with Team Members
Confluence: Configuring Spaces
Slack Web: Signing in and Setting Up
Slack Web: Using Channels
Slack 2016: Creating. Finding, & Sharing Information
Slack 2016: Configuring Slack
Slack iOS: Using the iOS App

Learners need a computer, laptop or tablet and internet connection. Courses are designed in video with audio and coupled with unlimited advanced mentoring.

ESTIMATED DURATION: 40-50 hours per Track, therefore should you study 1 hour per day you could complete the first Track in less than 2 months.

COURSE FEE: R10 995.00 cash or on terms R11 495.00 with R4 955.00 deposit and 12 monthly instalments of R545.00.

To register for the Data Analyst to Data Science course please click here.


On enrolment we issue our learners with an username and password to access their courses for a 12 month subscription. At the end of each module you will be required to answer multiple choice questions, these marks are registered in our database. Should you obtain 70% and above you will receive IT Academy Course Mastery Certification by mail, at no additional cost.

International exams are written at any Pearson Vue Testing Centre in South Africa. Discounted international exam vouchers can be purchased through IT Academy at R550.00 each.


Mobile ready (accessible on a Tablet or Smartphone)
Courses are printable
Unlimited Advanced Mentoring
e-mail Support
Free Cram Tests
Discounted international exam vouchers
12 months Subscription
IT Academy Course Mastery Certification



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