Data Analyst to Data Scientist

Course code: C01081

Course overview:

• Track 1: The focus is on the data analyst role with a focus on: Python, R, architecture, statistics, and Spark.
• Track 2: The focus is on the data wrangler role. We will explore areas such as: wrangling with Python, Mongo, and Hadoop.
• Track 3: The focus is on the Data Ops role. Here we will explore areas such as: governance, security, and harnessing volume and velocity.
• Track 4: The focus is on the Data Scientist role. Here we will explore areas such as: visualisation, APIs, and ML and DL algorithms.

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:

Data Science Track 1: Data Analyst

Data Engineering Fundamentals
Python for Data Science: Introduction to NumPy for Multi-dimensional 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: 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: 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: Introduction
Data Silos, Lakes, and Streams: Data Lakes on AWS
Data Silos, Lakes, & Streams: Sources, Visualizations, & ETL Operations
Data Analysis Application
Analyzing Data with Python (Lab)
Final Exam: Data Analyst

Data Science Track 2: Data Wrangler

Mentoring Data Science Journey
Data Wrangling with Pandas: Working with Series & Data
Frames Data Wrangling with Pandas: Visualizations and Time-Series Data
Data Wrangling with Pandas: Advanced Features
Data Wrangler 4: Cleaning Data in R
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: 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 Executions
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: 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

Data Science Track 3: Data Ops

Deploying Data Tools: Data Science Tools
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 Oversight and IAM
Data Access & Governance Policies: Data Classification, Encryption, and Monitoring
Streaming Data Architectures: An Introduction to Streaming Data
Streaming Data Architectures: Processing Streaming Data
Scalable Data Architectures: Introduction
Scalable Data Architectures: Introduction to Amazon Redshift
Scalable Data Architectures: Working with Amazon Redshift & Quick Sight Building Data Pipelines
Data Pipeline: Process Implementation Using Tableau & AWS
Data Pipeline: Using Frameworks for Advanced Data Management
Data Sources: Integration
Data Sources: Implementing Edge on the Cloud Securing Big Data Streams
Harnessing Data Volume & Velocity: Big Data to Smart Data
Data Rollbacks: Transaction Rollbacks & Their Impact
Data Rollbacks: Transaction Management & Rollbacks in NoSQL
Implementing Data Ops with Python (Lab)
Final Exam: Data Ops

Data Science Track 4: Data Scientist

Balancing the Four Vs of Data: 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 & PowerBI
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
Advanced Visualizations & Dashboards: Visualization Using Python
Advanced Visualizations & Dashboards: Visualization Using R
Powering Recommendation Engines: 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
Data Visualization with Python (Lab)
Final Exam: Data Scientist