Data Analyst Career Path - ITU Online

Data Analyst Career Path

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Elevate your career with our Data Analyst Training Series. Master SQL, Excel, Power BI, and big data analytics to become a proficient Data Analyst. Ideal for aspiring analysts and professionals seeking to deepen their data skills in a practical, real-world context.

Original price was: $129.00.Current price is: $51.60.

This Data Analyst Career Path Features

hours
56 Hrs 36 Min
Videos
358 On-demand Videos
Closed Captions

Closed Captions

Course Topics
59  Topics
Question & Answers
429 Prep Questions
Certificate of Completion

Certificate of Completion

The Job Role: Mapping the Data Analyst Career Journey

Diving into the data analyst career path, we find that a Data Analyst is the Sherlock Holmes of the digital world, tasked with the mission of interpreting data to deduce valuable insights for informed decision-making. This professional is the bridge between raw data and strategic outcomes, wielding tools like Microsoft Power BI, Excel, and SQL Server as deftly as a maestro conducts an orchestra. Their role is a mosaic of responsibilities, each tile a crucial part of the larger data management and analysis picture.

  • Data Collection and Management: On the analyst career path, our data detectives collect and safeguard data from various sources, ensuring its purity with meticulous cleaning and preprocessing—because in data, as in life, cleanliness is next to godliness.
  • Data Analysis and Interpretation: With the career progression for a data analyst, interpreting data goes beyond mere number crunching; it’s about identifying the rhythm in the numbers, discerning trends, and composing ongoing narratives that propel businesses forward.
  • Database Management and Querying: Utilizing SQL Server is not just about managing databases; it’s an art of creating and querying to extract the most pertinent information—akin to finding the proverbial needle in the haystack.
  • Data Visualization and Reporting: The path to becoming a data analyst often leads to creating compelling visualizations with tools like Microsoft Power BI. It’s about translating complex data findings into visual stories that even the most data-averse can understand.
  • Excel Proficiency: Excel is to a data analyst what a wand is to a wizard in the magical realm of data manipulation. Formula application, pivot tables, and data manipulation are just the beginning of the sorcery on the data analytics career track.
  • Big Data Technologies: In the quest for career growth for a data analyst, understanding and applying big data technologies is like navigating a vast ocean of information with the precision of a seasoned captain.
  • Advanced Analysis Techniques: Advanced data analysis techniques, such as those covered in SQL Server 2019 Analysis Services (SSAS), are the secret spices that elevate the gourmet dish of data processing and analytics.
  • Problem Solving: A data analyst’s progression involves using analytical prowess not just to solve puzzles but to weave the very fabric of business solutions and recommendations.
  • Collaboration and Communication: Data analysts must work in harmony with various teams, understanding their data symphonies and conducting findings into impactful results.
  • Continual Learning and Skill Upgradation: The data analytics career path is an eternal school, where staying current with the ever-evolving tools and technologies is the only way to maintain a valedictorian status.

Course: 1 - Estimated 2 Week(s) To Complete
Microsoft SQL Server - Introduction to Data Analysis Course Content
13 Hours 58 Minutes 77 Videos 75 Prep Questions
This course provides foundational knowledge in using SQL Server for data analysis, covering how to manage and analyze large data sets. It is crucial for Data Analysts to understand database management and data manipulation techniques.

Module 1 - Query Tools
   1.1 Course Introduction
   1.2 Intro to Management Studio
   1.3 Intro to command-line query tools

Module 2 - Introduction to T-SQL Querying
   2.1 Introducing T-SQL
   2.2 Understanding Sets
   2.3 Understanding the Logical Order of Operations in SELECT statements

Module 3 - Basic SELECT Queries
   3.1 Writing Simple SELECT Statements
   3.2 Eliminate Duplicates with DISTINCT
   3.3 Using Column and Table Aliases
   3.4 Write Simple CASE Expressions

Module 4 - Querying Multiple Tables
   4.1 Understanding Joins
   4.2 Querying with Inner Joins
   4.3 Querying with Outer Joins
   4.4 Querying with Cross Joins and Self Joins

Module 5 - Sorting and Filtering Data
   5.1 Sorting Data
   5.2 Filtering Data with Predicates
   5.3 Filtering with the TOP and OFFSET-FETCH
   5.4 Working with Unknown Values

Module 6 - Introduction to Business Intelligence and Data Modeling
   6.1 Introduction to Business Intelligence
   6.2 The Microsoft Business Intelligence Platform
   6.3 Exploring a Data Warehouse
   6.4 Exploring a Data Model

Module 7 - Prepare Data
   7.1 Introduction to Power BI
   7.2 Get data from various data sources
   7.3 Preview source data

Module 8 - Clean, Transform, and Load Data
   8.1 Data Transformation Intro
   8.2 Transformation Example 1
   8.3 Transformation Example 2
   8.4 Transformation Example 3
   8.5 Transformation Example 4
   8.6 Transformation Example 5
   8.7 Transformation Example 6

Module 9 - Design a Data Model
   9.1 Introduction to Data Modeling
   9.2 Model Relationships
   9.3 Table Configuration
   9.4 Model interface
   9.5 Quick Measures
   9.6 Many-to-many relationships
   9.7 Row-level security

Module 10 - Create Model Calculations using DAX
   10.1 DAX context
   10.2 Calculated Tables
   10.3 Calculated Columns
   10.4 Managing Date Tables
   10.5 Measures
   10.6 Filter Manipulation
   10.7 Time Intelligence

Module 11 - Create Reports
   11.1 Basic Report Creation
   11.2 Example Page 1
   11.3 Example Page 2
   11.4 Example Page 3
   11.5 Report Publishing
   11.6 Enhancing Reports
   11.7 Drill-Through Pages
   11.8 Conditional Formatting
   11.9 Buttons and Bookmarks

Module 12 - Create Dashboards
   12.1 Dashboard Basics
   12.2 Real Time Dashboards
   12.3 Enhanced Dashboards

Module 13 - Create Paginated Reports
   13.1 Introduction to Power BI Report Builder
   13.2 Report Layouts
   13.3 Report Data
   13.4 Report Tables

Module 14 - Perform Advanced Analytics
   14.1 Introduction to Advanced Analytics
   14.2 Scatter Chart
   14.3 Forecast
   14.4 Decomposition Tree
   14.5 Key Influencers

Module 15 - Create and Manage Workspaces
   15.1 Introduction to Workspaces
   15.2 Working with Workspaces and the Portal

Module 16 - Create Power App Visuals
   16.1 Introduction to Power Apps Visual
   16.2 Creating the App
   16.3 Basic Power Apps Concepts
   16.4 Refreshing the Report

Module 17 - Analysis Services and Power BI
   17.1 Introduction to Analysis Services
   17.2 Connecting with Multidimensional Models
   17.3 Premium Workspaces and Analysis Services
   17.4 Course Wrap Up

Course: 2 - Estimated 2 Week(s) To Complete
Microsoft SQL Server - Querying SQL Server Course Content
10 Hours 05 Minutes 67 Videos 75 Prep Questions
This course focuses on writing and optimizing SQL queries, a core skill for Data Analysts. It teaches how to retrieve and manipulate data efficiently from databases, which is essential for data analysis tasks.

Module 1 - Query Tools
   1.1 Course Introduction
   1.2 Module 1 Introduction
   1.3 Intro to Management Studio
   1.4 Intro to command-line query tools

Module 2 - Introduction to T-SQL Querying
   2.1 Module 2 Introduction
   2.2 Introducing T-SQL
   2.3 Understanding Sets
   2.4 Understanding the Logical Order of Operations in SELECT statements

Module 3 - Basic SELECT Queries
   3.1 Module 3 Introduction
   3.2 Writing Simple SELECT Statements
   3.3 Eliminate Duplicates with DISTINCT
   3.4 Using Column and Table Aliases
   3.5 Write Simple CASE Expressions

Module 4 - Querying Multiple Tables
   4.1 Module 4 Introduction
   4.2 Understanding Joins
   4.3 Querying with Inner Joins
   4.4 Querying with Outer Joins
   4.5 Querying with Cross Joins and Self Joins

Module 5 - Sorting and Filtering Data
   5.1 Module 5 Introduction
   5.2 Sorting Data
   5.3 Filtering Data with Predicates
   5.4 Filtering with the TOP and OFFSET-FETCH
   5.5 Working with Unknown Values

Module 6 - Working with SQL Server Data Types
   6.1 Module 6 Introduction
   6.2 Writing Queries that return Date and Time Data
   6.3 Writing Queries that use Date and Time Functions
   6.4 Writing Queries that return Character Data
   6.5 Writing Queries that use Character Functions

Module 7 - Using DML to Modify Data
   7.1 Module 7 Introduction
   7.2 Inserting Records with DML
   7.3 Updating Records Using DML
   7.4 Deleting Records Using DML

Module 8 - Using Built-In Functions
   8.1 Module 8 Introduction
   8.2 Writing Queries with Built-In Functions
   8.3 Using Conversion Functions
   8.4 Using Logical Functions
   8.5 Using Functions to Work with NULL

Module 9 - Grouping and Aggregating Data
   9.1 Module 9 Introduction
   9.2 Using Aggregate Functions
   9.3 Using the GROUP BY Clause
   9.4 Filtering Groups with HAVING

Module 10 - Using Subqueries
   10.1 Module 10 Introduction
   10.2 Writing Self-Contained Subqueries
   10.3 Writing Correlated Subqueries
   10.4 Using the EXISTS Predicate with Subqueries

Module 11 - Using Table Expressions
   11.1 Module 11 Introduction
   11.2 Using Views
   11.3 Using Inline Table-Valued Functions
   11.4 Using Derived Tables
   11.5 Using Common Table Expressions

Module 12 - Using Set Operators
   12.1 Module 12 Introduction
   12.2 Writing Queries with the UNION operator
   12.3 Using EXCEPT and INTERSECT
   12.4 Using APPLY

Module 13 - Using Window Ranking, Offset, and Aggregate Functions
   13.1 Module 13 Introduction
   13.2 Creating Windows with OVER
   13.3 Exploring Window Functions

Module 14 - Pivoting and Grouping Sets
   14.1 Module 14 Introduction
   14.2 Writing Queries with PIVOT and UNPIVOT
   14.3 Working with Grouping Sets

Module 15 - Implementing Error Handling
   15.1 Module Introduction
   15.2 Implementing T-SQL error handling
   15.3 Implementing structured exception handling

Module 16 - Managing Transactions
   16.1 Module 16 Introduction
   16.2 Transactions and the Database Engine
   16.3 Controlling Transactions
   16.4 Course Wrap Up

Course: 3 - Estimated 2 Week(s) To Complete
Introduction to Microsoft Power BI Course Content
10 Hours 52 Minutes 65 Videos 75 Prep Questions
In this course, participants learn how to use Power BI for creating dynamic data visualizations and dashboards. These skills are vital for Data Analysts to present data insights in an accessible and impactful way.

Module 1 - Prepare Data
   1.1 Course Introduction
   1.2 Module 1 Introduction
   1.3 Introduction to Power BI
   1.4 Get data from various data sources
   1.5 Preview source data

Module 2 - Clean, Transform, and Load Data
   2.1 Module 2 Introduction
   2.2 DimEmployee Example
   2.3 DimEmployeeSalesTerritory Example
   2.4 DimReseller Example
   2.5 FactResellersSales Example
   2.6 ResellerSalesTargets Example
   2.7 Color Formats Example

Module 3 - Design a Data Model
   3.1 Module 3 Introduction
   3.2 Introduction to Data Modeling
   3.3 Model Relationships
   3.4 Table Configuration
   3.5 Model interface
   3.6 Quick Measures
   3.7 Many-to-many relationships
   3.8 Row-level security

Module 4 - Create Model Calculations using DAX
   4.1 Module 4 Introduction
   4.2 DAX context
   4.3 Calculated Tables
   4.4 Calculated Columns
   4.5 Managing Date Tables
   4.6 Measures
   4.7 Filter Manipulation
   4.8 Time Intelligence

Module 5 - Create Reports
   5.1 Module 5 Introduction
   5.2 Basic Report Creation
   5.3 Example Page 1
   5.4 Example Page 2
   5.5 Example Page 3
   5.6 Report Publishing
   5.7 Enhancing Reports
   5.8 Drill-Through Pages
   5.9 Conditional Formatting
   5.10 Buttons and Bookmarks

Module 6 - Create Dashboards
   6.1 Module 6 Introduction
   6.2 Dashboard Basics
   6.3 Real Time Dashboards
   6.4 Enhanced Dashboards

Module 7 - Create Paginated Reports
   7.1 Module 7 Introduction
   7.2 Introduction to Power BI Report Builder
   7.3 Report Layouts
   7.4 Report Data
   7.5 Report Tables

Module 8 - Perform Advanced Analytics
   8.1 Module 8 Introduction
   8.2 Introduction to Advanced Analytics
   8.3 Scatter Chart
   8.4 Forecast
   8.5 Decomposition Tree
   8.6 Key Influencers

Module 9 - Create and Manage Workspaces
   9.1 Introduction to Workspaces
   9.2 Working with Workspaces and the Portal

Module 10 - Create Power App Visuals
   10.1 Module 10 Introduction
   10.2 Introduction to Power Apps Visual
   10.3 Creating the App
   10.4 Basic Power Apps Concepts
   10.5 Refreshing the Report

Module 11 - Analysis Services and Power BI
   11.1 Module 11 Introduction
   11.2 Introduction to Analysis Services
   11.3 Connecting with Multidimensional Models
   11.4 Premium Workspaces and Analysis Services
   11.5 Course Wrap Up

Course: 4 - Estimated 1 Week(s) To Complete
Microsoft Excel Course Content
05 Hours 37 Minutes 58 Videos 54 Prep Questions
This course covers advanced Excel techniques, including data organization, analysis, and visualization. Excel is a fundamental tool for Data Analysts, widely used for its versatility in various data processing and analytical tasks.

Module 1: Beginner
   1.0 Intro
   1.1 The Ribbon
   1.2 Saving Files
   1.3 Entering and Formatting Data
   1.4 Printing from Excel & Using Page Layout View
   1.5 Formulas Explained
   1.6 Working with Formulas and Absolute References
   1.7 Specifying and Using Named Range
   1.8 Correct a Formula Error
   1.9 What is a Function
   1.10 Insert Function & Formula Builder
   1.11 How to Use a Function- AUTOSUM, COUNT, AVERAGE
   1.12 Create and Customize Charts

Module 2: Intermediate
   2.0 Recap
   2.1 Navigating and editing in two or more worksheets
   2.2 View options - Split screen, view multiple windows
   2.3 Moving or copying worksheets to another workbook
   2.4 Create a link between two worksheets and workbooks
   2.5 Creating summary worksheets
   2.6 Freezing Cells
   2.7 Add a hyperlink to another document
   2.8 Filters
   2.9 Grouping and ungrouping data
   2.10 Creating and customizing all different kinds of charts
   2.11 Adding graphics and using page layout to create visually appealing pages
   2.12 Using Sparkline formatting
   2.13 Converting tabular data to an Excel table
   2.14 Using Structured References
   2.15 Applying Data Validation to cells
   2.16 Comments - Add, review, edit
   2.17 Locating errors

Module 3: Advanced
   3.1 Recap
   3.2 Conditional (IF) functions
   3.3 Nested condition formulas
   3.4 Date and Time functions
   3.5 Logical functions
   3.6 Informational functions
   3.7 VLOOKUP & HLOOKUP
   3.8 Custom drop down lists
   3.9 Create outline of data
   3.10 Convert text to columns
   3.11 Protecting the integrity of the data
   3.12 What is it, how we use it and how to create a new rule
   3.13 Clear conditional formatting & Themes
   3.14 What is a Pivot Table and why do we want one
   3.15 Create and modify data in a Pivot Table
   3.16 Formatting and deleting a Pivot Table
   3.17 Create and modify Pivot Charts
   3.18 Customize Pivot Charts
   3.19 Pivot Charts and Data Analysis
   3.20 What is it and what do we use it for
   3.21 Scenarios
   3.22 Goal Seek
   3.23 Running preinstalled Macros
   3.24 Recording and assigning a new Macro
   3.25 Save a Workbook to be Macro enabled
   3.26 Create a simple Macro with Visual Basics for Applications (VBA)
   3.27 Outro

Course: 5 - Estimated 1 Week(s) To Complete
Microsoft SQL Server - Big Data Course Content
07 Hours 06 Minutes 41 Videos 75 Prep Questions
This course delves into managing and analyzing big data with SQL Server. It equips Data Analysts with the skills to handle large and complex data sets, a growing necessity in many industries.

Module 1: What are Big Data Clusters?
   1.1 Introduction
   1.2 Linux, PolyBase, and Active Directory
   1.3 Scenarios

Module 2: Big Data Cluster Architecture
   2.1 Introduction
   2.2 Docker
   2.3 Kubernetes
   2.4 Hadoop and Spark
   2.5 Components
   2.6 Endpoints

Module 3: Deployment of Big Data Clusters
   3.1 Introduction
   3.2 Install Prerequisites
   3.3 Deploy Kubernetes
   3.4 Deploy BDC
   3.5 Monitor and Verify Deployment

Module 4: Loading and Querying Data in Big Data Clusters
   4.1 Introduction
   4.2 HDFS with Curl
   4.3 Loading Data with T-SQL
   4.4 Virtualizing Data
   4.5 Restoring a Database

Module 5: Working with Spark in Big Data Clusters
   5.1 Introduction
   5.2 What is Spark
   5.3 Submitting Spark Jobs
   5.4 Running Spark Jobs via Notebooks
   5.5 Transforming CSV
   5.6 Spark-SQL
   5.7 Spark to SQL ETL

Module 6: Machine Learning on Big Data Clusters
   6.1 Introduction
   6.2 Machine Learning Services
   6.3 Using MLeap
   6.4 Using Python
   6.5 Using R

Module 7: Create and Consume Big Data Cluster Apps
   7.1 Introduction
   7.2 Deploying, Running, Consuming, and Monitoring an App
   7.3 Python Example - Deploy with azdata and Monitoring
   7.4 R Example - Deploy with VS Code and Consume with Postman
   7.5 MLeap Example - Create a yaml file
   7.6 SSIS Example - Implement scheduled execution of a DB backup

Module 8: Maintenance of Big Data Clusters
   8.1 Introduction
   8.2 Monitoring
   8.3 Managing and Automation
   8.4 Course Wrap Up

Course: 6 - Estimated 1 Week(s) To Complete
Microsoft SQL Server Analysis Services (SSAS) Course Content
08 Hours 56 Minutes 50 Videos 75 Prep Questions
This course focuses on using SSAS for developing and deploying analytics solutions. For Data Analysts, mastering SSAS is crucial for performing advanced data analysis and creating robust business intelligence solutions.

Module 1 - Introduction to Business Intelligence and Data Modeling
   1.1 Course Introduction
   1.2 Module 1 Introduction
   1.3 Introduction to Business Intelligence
   1.4 The Microsoft Business Intelligence Platform
   1.5 Exploring a Data Warehouse
   1.6 Exploring a Data Model

Module 2 - Multidimensional Databases
   2.1 Module 2 Introduction
   2.2 Introduction to Multidimensional Analysis
   2.3 Overview of Cube Security
   2.4 Creating and Configuring a Cube
   2.5 Data Sources
   2.6 Data Source Views
   2.7 Adding a Dimension to a Cube

Module 3 - Cubes and Dimensions
   3.1 Module 3 Introduction
   3.2 Dimensions
   3.3 Attribute Hierarchies and Relationships
   3.4 Sorting and Grouping Attributes
   3.5 Slowly Changing Dimensions

Module 4 - Measures and Measure Groups
   4.1 Module 4 Introduction
   4.2 Measures
   4.3 Measure Groups and Relationships
   4.4 Measure Group Storage

Module 5 - Introduction to MDX
   5.1 Module 5 Introduction
   5.2 MDX Fundamentals
   5.3 Adding Calculations to a Cube
   5.4 Querying a cube using MDX

Module 6 - Customizing Cube Functionality
   6.1 Module 6 Introduction
   6.2 Key Performance Indicators
   6.3 Actions
   6.4 Perspectives
   6.5 Translations

Module 7 - Tabular Data Models
   7.1 Module 7 Introduction
   7.2 Introduction to Tabular Data Models
   7.3 Creating a Tabular Data Model
   7.4 Configure Relationships and Attributes
   7.5 Configuring Data Model for an Enterprise BI Solution

Module 8 - Data Analysis Expressions (DAX)
   8.1 Module 8 Introduction
   8.2 DAX Fundamentals
   8.3 Calculated Columns
   8.4 Relationships
   8.5 Measures
   8.6 Time Intelligence
   8.7 KPI
   8.8 Parent - Child Hierarchies

Module 9 - Data Mining
   9.1 Module 9 Introduction
   9.2 Overview of Data Mining
   9.3 Custom Data Mining Solutions
   9.4 Validating a Data Mining Model
   9.5 Consuming a Data Mining Model
   9.6 Course Wrap Up

In the era where data is the new oil, the need for adept Data Analysts to drill into this valuable resource is at an all-time high. Our Data Analyst Career Path training series is the map for this treasure hunt, offering an exhaustive educational voyage for those aspiring to master the craft of data analysis. This series is a comprehensive exploration of the theoretical and practical facets of data analytics, designed to arm participants with the armor needed to transform data into actionable insights in diverse business landscapes.

This career path for an analyst is intricate and dynamic, traversing from the foundational elements of data collection and management to the nuanced realms of advanced analysis and reporting. Our training series uncovers the secrets of these domains, preparing participants for various roles within the analytics field. The curriculum is a carefully curated labyrinth, guiding learners through the essentials of Microsoft Power BI, SQL Server, and Excel, alongside the creative domains of data visualization and big data technologies.

For those standing at the crossroads of their career, pondering paths such as “What does a Data Analyst do?” or “What are the requirements to become a Data Analyst?”, this series lights the torches through the caverns of uncertainty with hands-on learning and real-world applications.

Moreover, this series is the starting point for those ready to embark on the data analytics career path. It clarifies the fundamentals of “What is a Data Analyst?” and “What qualifications are needed for a Data Analyst?”, delineating a clear trajectory towards acquiring these qualifications. The courses are tailored to resonate with the current job market demands and also to stay updated with the emergent trends in this ever-evolving domain.

The training goes beyond the core curriculum, providing glimpses into the myriad opportunities within the field, like roles in business intelligence and market research. It equips learners with the necessary qualifications and competencies to thrive in these niches, making it an essential repository for anyone looking to carve out a successful career in the data analytics landscape.

Our Data Analyst Career Path training series is a cruise ship for a diverse array of voyagers eager to explore the seas of data analysis. Here’s a compass for those who will find this journey most enlightening:

  • Aspiring Data Analysts: For the novices at the beginning of their expedition, this series is the foundational atlas. It’s perfect for those setting sail towards roles like Business Data Analysts or Statistical Analysts.
  • Career Shifters: Those looking to navigate their careers into the waters of data analytics will find this training series a lifeboat, providing a safe transition into a new career trajectory.
  • Current Data Professionals Aiming for Growth: Seasoned sailors of the data sea seeking to upgrade their skills, specialize, or climb the career mast will find the wind in their sails with the advanced knowledge this series offers.
  • Students and Recent Graduates: Academics or fresh out of the harbor graduates will find that this series adds practical sails to their theoretical boat, increasing their navigational skills for roles related to data analysis.
  • Freelancers and Independent Consultants: Solo adventurers providing freelance data analysis services will discover a treasure trove of knowledge, augmenting their ability to serve a diverse clientele.
  • Business Owners and Entrepreneurs: Captains of industry who wish to steer their own ship through the use of strategic data will find the training a lighthouse, guiding the way to effective data analytics leadership.
  • Enthusiasts and Hobbyists: Even those not charting a professional course in data analysis but with a passion for the field will find this series a delightful and educational voyage into the world of data analytics.

In essence, this training series welcomes aboard a vast audience, from fresh-faced recruits to battle-hardened veterans, and is particularly beneficial for anyone wishing to establish or advance their voyage on the dynamic and ever-expanding ocean of data analytics.

What qualifications are necessary to become a Data Analyst?

Typically, a bachelor’s degree in data science, statistics, computer science, or a related field is required. Proficiency in data analysis tools like SQL, Excel, and Power BI, along with strong analytical and problem-solving skills, are essential. Some roles may also require knowledge of programming languages like Python or R.

What does a Data Analyst do on a daily basis?

Daily tasks include collecting and interpreting data, performing analysis to identify trends and insights, creating visualizations and reports, and communicating findings to stakeholders. Data Analysts also regularly clean and validate data to ensure accuracy and work on improving data collection and analysis processes.

How does a Data Analyst differ from a Data Scientist?

Data Analysts typically focus on interpreting existing data to provide actionable insights, often using tools like SQL and Excel. Data Scientists, on the other hand, build more complex models, often using machine learning, and are involved in predicting future trends from data. They usually require a deeper knowledge of programming and statistics.

What are the career advancement opportunities for a Data Analyst?

Data Analysts can advance to senior analyst roles, become Data Scientists, or specialize in areas like business intelligence or data engineering. With experience, they can also move into managerial roles, like Data Analytics Manager or Chief Data Officer.

What industries employ Data Analysts?

Data Analysts are employed across a wide range of industries, including finance, healthcare, technology, retail, marketing, and government. The demand for data analytics skills is widespread and not limited to a specific sector.

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4.8
Based on 82 reviews
1-5 of 82 reviews
  1. SJ

    good

  2. AA
  3. K
  4. BO
  5. A

    Smooth delivery and easy access to LMS. Good to see that the LMS offers progress tracking. Would be great if badges were offered on completion of courses to share via Credly to future employers.