Data Analyst to Data Scientist

Course fee

Cost: R7 200.00

Deposit: R1 800.00

Monthly instalment: R900.00 x 6

Duration: You will have Skillsoft access to this course for 12 months. The average time required to work through the syllabus is:

• 101 courses (88h 22m)
• Optional additional resources are available to enhance your learning in your own time.

About the course

Course code: C01081

Course overview:

• Track 1: Data Analyst
• Track 2: Data Wrangler
• Track 3: Data Ops
• Track 4: Data Scientist

What are Aspire Journeys?

Aspire Journeys are guided learning paths designed and published by Skillsoft. These courses provide:

• A clear starting point across the roles and responsibilities of tomorrow.
• Exercises for on-the-job applications to put what you’ve learned into practice.
• Verifiable, shareable, and portable digital badges so you can celebrate accomplishments along the way.
• A diverse array of learning tools from the books to audiobooks to video courses, and more.

The learning path for each journey comprises tracks of content in a recommended order. Completing all content within a track completes the track. Completing all tracks within the journey completes the journey.

Modules and topics covered:

Track 1: Data Analyst

• Data Architecture Getting Started
• Data Engineering Getting Started
• Python – Introduction to NumPy for Multi-dimensional Data
• Python – Advanced Operations with NumPy Arrays
• Python – Introduction to Pandas and DataFrames
• Python – Manipulating & Analyzing Data in Pandas DataFrames
• R Data Structures
• Importing & Exporting Data using R
• Data Exploration using R
• R Regression Methods
• R Classification & Clustering
• Simple Descriptive Statistics
• Common Approaches to Sampling Data
• Inferential Statistics
• Apache Spark Getting Started
• Hadoop & MapReduce Getting Started
• Developing a Basic MapReduce Hadoop Application
• Hadoop HDFS Getting Started
• Introduction to the Shell for Hadoop HDFS
• Working with Files in Hadoop HDFS
• Hadoop HDFS File Permissions
• Data Silos, Lakes, & Streams Introduction
• Data Lakes on AWS
• Data Lake Sources, Visualizations, & ETL Operations
• Applied Data Analysis
• Final Exam: Data Analyst

Track 2: Data Wrangler

• Python – Using Pandas to Work with Series & DataFrames
• Python – Using Pandas for Visualizations and Time-Series Data
• Python – Pandas Advanced Features
• Cleaning Data in R
• Technology Landscape & Tools for Data Management
• Machine Learning & Deep Learning Tools in the Cloud
• Data Wrangling with Trifacta
• MongoDB Querying
• MongoDB Aggregation
• Getting Started with Hive
• Loading & Querying Data with Hive
• Viewing & Querying Complex Data with Hive
• Optimizing Query Executions with Hive
• Using Hive to Optimize Query Executions with Partitioning
• Bucketing & Window Functions with Hive
• Filtering Data Using Hadoop MapReduce
• Hadoop MapReduce Applications With Combiners
• Advanced Operations Using Hadoop MapReduce
• Data Analysis Using the Spark DataFrame API
• Data Analysis using Spark SQL
• Data Lake Framework & Design Implementation
• Data Lake Architectures & Data Management Principles
• Data Architecture Deep Dive – Design & Implementation
• Data Architecture Deep Dive – Microservices & Serverless Computing
• Final Exam: Data Wrangler

Track 3: Data Ops

• Data Science Tools
• Delivering Dashboards: Management Patterns
• Delivering Dashboards: Exploration & Analytics
• Cloud Data Architecture: Cloud Architecture & Containerization
• Cloud Data Architecture: Data Management & Adoption Frameworks
• Data Compliance Issues & Strategies
• Implementing Governance Strategies
• Data Access & Governance Policies: Data Access Governance & IAM
• Data Access & Governance Policies: Data Classification, Encryption, & Monitoring
• Streaming Data Architectures: An Introduction to Streaming Data in Spark
• Streaming Data Architectures: Processing Streaming Data with Spark
• Scalable Data Architectures: Getting Started
• Scalable Data Architectures: Using Amazon Redshift
• Scalable Data Architectures: Using Amazon Redshift & QuickSight
• Building Data Pipelines
• Data Pipeline: Process Implementation Using Tableau & AWS
• Data Pipeline: Using Frameworks for Advanced Data Management
• Data Sources: Integration from the Edge
• Data Sources: Implementing Edge Data on the Cloud
• Securing Big Data Streams
• Harnessing Data Volume & Velocity: Turning Big Data into Smart Data
• Data Rollbacks: Transaction Rollbacks & Their Impact
• Data Rollbacks: Transaction Management & Rollbacks in NoSQL
• Final Exam: Data Ops

Track 4: Data Scientist

• The Four Vs of Data
• Data Driven Organizations
• Raw Data to Insights: Data Ingestion & Statistical Analysis
• Raw Data to Insights: Data Management & Decision Making
• Tableau Desktop: Real Time Dashboards
• Storytelling with Data: Introduction
• Storytelling with Data: Tableau & Power BI
• 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
• Advanced Visualizations & Dashboards: Visualization Using Python
• R for Data Science: Data Visualization
• Advanced Visualizations & Dashboards: Visualization Using R
• Data Recommendation Engines
• Data Insights, Anomalies, & Verification: Handling Anomalies
• Data Insights, Anomalies, & Verification: Machine Learning & Visualization Tools
• Data Science Statistics: Applied Inferential Statistics
• Data Research Techniques
• Data Research Exploration Techniques
• Data Research Statistical Approaches
• Machine & Deep Learning Algorithms: Introduction
• Machine & Deep Learning Algorithms: Regression & Clustering
• Machine & Deep Learning Algorithms: Data Preparation in Pandas ML
• Machine & Deep Learning Algorithms: Imbalanced Datasets Using Pandas ML
• Creating Data APIs Using Node.js
• Final Exam: Data Scientist

Admission requirements

Academic grade: No minimum school pass requirements or formal prerequisites, but it is recommended that candidates have some experience in the lab or field.

Language: Proficiency in English (course material and support only available in English).

Expertise level: Intermediate

Equipment: Access to a PC or laptop with a reliable internet connection.

Effort: Self-paced learning online.

Accreditation status

Course type: Short course

Industry partner: Skillsoft

Certification: Certificate confirming course completion.

Certification issued by: College SA

Assessment details

Each track concludes with a final internal exam that will test your knowledge and application of the topics presented throughout that specific track. There are no external certification exams for this course.

What to expect

Dedicated support team

We understand that students may require guidance and support to navigate the learning journey, and our Client Services team is always ready to assist them in every possible way. Our team is readily available during office hours and can be contacted via email, phone, WhatsApp and social media.

Skillsoft Learner Management System (LMS) access

Skillsoft is an online learning management system that offers all students enrolled for any of our IT Academy courses compelling content, interactive videos, quizzes, mentoring and practical simulations/virtual labs. The platform allows students to learn at their own pace.