Data Analyst to Data Scientist

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