The application of data analytics across industries is exploding. As organizations accumulate ever-growing data resources, demand has rapidly emerged for analytics talent who can derive critical insights from data to inform strategic decisions and drive growth.
This guide will overview top-rated learning options for quickly developing in-demand data analytics qualifications to take advantage of high-salary career opportunities in this cutting-edge domain.
The Growing Business Imperative for Data Analytics
More data has been created in the past 2 years than in the entire previous history of humankind. Analyst firm IDC predicts global data creation to continue expanding at staggering rates, reaching 175 zettabytes by 2025. Modern enterprises are aggregating data faster than they can extract meaning from it.
This data deluge urgently necessitates analytics talent and technologies to efficiently distill insights that confer competitive advantages. A recent McKinzey survey found that:
- 63% of executives say analytics creates business value
- High performing companies are 5X more likely to use analytics strategically
- Analytics talent shortages remain top barriers to gaining value from data
The mission-critical status of analytics across industries demands qualified practitioners who can strategically apply statistical, technological, and business acumen to capitalize on burgeoning data resources.
Lucrative Careers Await Qualified Analytics Professionals
Scarcity of qualified talent combined with extreme business demand produces optimal conditions for lucrative analytics careers. Data scientists and engineers rank among the top 5 highest paying tech jobs.
Analytics positions offer escalating salary prospects based on experience levels according to recent data:
Entry Level
- Data Analyst – $72k
- Business Analyst – $68k
Mid-Level
- Data Engineer – $120k
- Analytics Manager – $135k
Advanced
- Data Scientist – $150k
- Machine Learning Engineer – $163k
Beyond compensation, analytics roles confer interesting work with lots of variety in terms of data, technologies, business domains and opportunities to uncover impactful insights.
Now let‘s overview some of the most in-demand analytics jobs current dominating industry hiring demands:
Data Analyst
- Query, analyze & report on data to discover insights
- Tech: SQL, Excel, Tableau, Statistics
- Entry-level position
Business Analyst
- Understand business operations to identify improvement opportunities via data analysis
- Tech: SQL, Excel, Data Modeling, Analytics
- Entry/Associate position
Data Engineer
- Build data pipelines to feed downstream analysis
- Tech: Databases, Cloudplatforms, ETL Frameworks
- Mid/Senior-level position
Data Scientist/Machine Learning Engineer
- Develop ML models to automate analytical insights
- Tech: Python, SQL, Statistics, Machine Learning
- Mid/Senior-level position
Analytics Manager
- Lead analytics teams to apply data quantitatively supporting business goals
- Tech: Data-informed critical thinking, team leadership
- Senior position
Now that the immense career opportunities around analytics are clear, let‘s explore formal learning options to start qualifying for these lucrative and interesting roles.
Charting Your Data Analytics Education Roadmap
Breaking into data analytics entails strategic education planning in terms of sequencing topics and allocating time to avoid getting overwhelmed. The key training areas to cover include:
1. Core Analytics Foundations
- Math – statistics, calculus, linear algebra
- Excel – formulas, pivot tables, lookup functions
- SQL – queries across databases
- Programming – Python or R
2. Data Infrastructure
- Cloud platforms – AWS, GCP, Azure
- Databases – PostgreSQL, MySQL, etc
- Data pipelines – Spark, Airflow, dbt
3. Analysis Applications
- Business intelligence – Tableau, Looker, PowerBI
- Advanced modeling – machine learning, deep learning
- Specialized analytics – financial, marketing, operational
4. Communication/Leadership
- Data storytelling and visualization
- Translating analytics into business impacts
- Influencing decisions with data-based insights
Using this framework, a recommended learning roadmap for aspiring analysts would cover:
0-6 months: Core analytics foundations via online courses
6-12 months: Data infrastructure and analysis applications via bootcamps/nanodegrees
1-2+ years: Specialize in domain analytics while working (e.g. marketing analytics)
Adjust timelines based on commitment levels and existing experience. But use the key training areas to guide course/program selection across beginner, intermediate and advanced phases.
Next let‘s highlight excellent educational options across these skill categories…
Top Data Analytics Courses
A. Core Analytics Foundations
1. Statistics and Probability for Data Science using Python (Udemy)
Firm grasp of statistical concepts empowers accurate interpretation and application of data analysis. This course explores all the statistical fundamentals like probability, hypothesis testing, regression modeling, Bayesian methods, and more using the Python language. The applied coding focus helps cement retention of concepts for practitioners.
Key Details:
- 8.5 hours HD video
- Certificate of completion
- 30-day money-back guarantee
2. Excel for Business Analysts Master Course (Udemy)
Excel remains a ubiquitous analytical toolkit across companies. This Master Class covers advanced formulas, visualizations, dashboarding, modeling and analysis applications tailored to elevate business analysts??? abilities.
Key Details:
- 9.5 hours on-demand video
- Real-world example models
- Practice activities
- Certificate of completion
- 30-day money-back guarantee
3. SQL Bootcamp Course (Udemy)
Structured Query Language skills enable extraction of meaning from vast databases. SQL Bootcamp packs 2 courses covering beginner and advanced queries, database manipulation, joins across multiple tables, and more.
Key Details:
- 18 hours video
- Exercises + solutions
- 2 practice projects
- Certificate of completion
- 30-day money-back guarantee
4. Data Scientist with Python Career Track (DataCamp)
For thoroughly learning Python programming tailored to data science applications, DataCamp???s signature career track specializes in hands-on data manipulation, analysis, modeling and visualization exercises with immediate coding feedback.
Key Details:
- 350+ video hours
- 400+ exercises with automated feedback
- Interactive console for coding alongside instructor
- Certificate of completion
- 14-day free trial
B. Data Engineering
5. Data Engineering with Google Cloud Professional Certificate (Coursera)
As enterprise data volumes explode, architects need to design data pipelines leveraging scalable cloud infrastructure. This 6-course certificate program developed by Google covers architecting data lakes, warehouses and machine learning pipelines on Google Cloud Platform (GCP).
Key Details:
- ~50 hours learning
- Interactive visual labs + real-case scenarios
- Practice quizzes + instructor support
- Shareable certificate upon completion
6. Data Engineer Nanodegree (Udacity)
For hands-on practice across the full data engineering stack, Udacity???s nanodegree uniquely provides access to a GPU workstation for working with massive datasets and prototyping production pipelines. The project-based curriculum explores big data collection, transformation, warehousing and workflow orchestration.
Key Details:
- ~200 hours of learning
- Real-world projects
- 1:1 project reviews
- Nanodegree credential
7. Modern Data Engineering Specialization (Coursera)
This 5-course series from Yandex (a top Russian tech company) offers intermediate training for production-ready data engineering across batch and streaming datasets leveraging tools like PostgreSQL, Clickhouse, Kafka, Flink, and more.
Key Details:
- ~100 hours of video lectures + articles
- Interactive coding assignments + quizzes
- Shareable certificate upon completion
C. Analysis Applications
8. Tableau Data Analytics Program (Udemy)
This definitive Tableau training program developed with Tableau explains key analysis and visualization features through hands-on video demonstrations with real datasets. Students finish ready for Tableau certification exams as well as professional application.
Key Details:
- 8.5 hours HD video
- Practice datasets & exercise files
- Real-life data projects
- Certificate of completion
- 30-day money-back guarantee
9. Taming Big Data with Spark SQL, DataFrames and Datasets (Udemy)
For tackling immense datasets, Apache Spark enables performant analytical queries across clusters. This intermediate course helps developers leverage PySpark (Python API) to handle big data using Spark SQL and DataFrames.
Key Details:
- 4 hours on-demand video
- Code templates and diagrams
- Certificate of completion
- 30-day money-back guarantee
10. Machine Learning Scientist with Python Career Track (DataCamp)
This 11-course career track covers machine learning foundations then advanced implementations using Python libraries like NumPy, Pandas, SciKit-Learn, Pytorch and TensorFlow. The interactive coding environment accelerates ML skill acquisition.
Key Details:
- 60+ hours interactive video
- 350+ coding exercises
- Certificate of completion
- 14-day free trial
11. AI Engineering with Python Nanodegree (Udacity)
This advanced program explores production machine learning model building, optimization and deployment using Python. The project-based curriculum covers artificial intelligence applications in speech recognition, computer vision, healthcare, and more.
Key Details:
- ~200 hours of learning
- Real-world AI projects
- Project reviews
- Nanodegree credential
D. Communication & Leadership
12. Storytelling with Data Specialization (Coursera)
This analytical communication series examines visualizing and effectively presenting data insights tailored to inform business strategy. The research-based curriculum improves data storytelling skills for persuasively conveying analytics.
Key Details:
- ~15 hours learning
- Interactive exercises + quizzes
- Shareable certificate upon completion
13. Advanced Analytics with R Specialization (emeritus)
This intermediate series from MIT equips analysts to apply the versatile R programming language to surface impactful insights. Courses explore machine learning, Bayesian analytics, simulation, and tools like Spark and H20 for large datasets.
Key Details:
- ~250 hours learning
- 2 proctored exams
- Shareable certificate
Alternative Learning Formats
Beyond standalone courses, data analytics hopefuls can also consider:
Coding Bootcamps
These full-time intensive training programs focus mainly on programming and hands-on job preparation across 12-26 week durations. Options in data science or analytics are available via General Assembly, Springboard, etc. if craving full immersion.
Masters Programs
For post-graduate training at academic institutions, STEM master???s degrees like data science, analytics or machine learning confer prestigious credentials advancing leadership prospects. Programs take 1-2 years to complete.
MOOCs (Massive Open Online Courses)
Platforms like Coursera, edX and Udacity host affordable multi-course learning paths with flexible pacing leading to reputable completion certificates and nanodegrees. MOOCs represent cost-effective middle-grounds between bootcamp intensity and master???s durations.
Prospective analysts should weight tradeoffs across program types based on career timeline, learning style and budget. But formal credentials signalling mastery of tools like Python or credentials like a nanodegree certificate carry value communicating skills to employers.
Building an Analytics Portfolio
Beyond acquiring skills via courses, dedicated students should also build portfolios exhibiting capabilities to share with prospective employers.
A portfolio illustrates hands-on data experiences and storytelling abilities using actual projects and findings.
Tips for developing a standout data portfolio:
- Analyze an industry dataset using SQL and visualization
- Train a machine learning model with documented code
- Dashboard key business metrics using BI tools
Feature at least 3 projects across skills like data engineering, analysis and technical communication. Use public data sources to enable sharing since actual company data remains private.
Portfolios demonstrate applied abilities beyond test scores or grades converting skills into business impacts. All aspiring analytics job seekers should dedicate time to assembling examples, code and presentations in order to shine in interviews by showing rather than just telling what you can offer.
Launch Your Analytics Career
This overview detailed multiple learning pathways across best-in-class platforms towards high income analytics occupations.
Use the key training areas and roadmap to chart courses introducing core competencies then reinforcing through practical specialized applications.
Complement formal learning with portfolio development exhibiting abilities analyzing real data and communicating insights.
The demand for qualified analysts, scientists and engineers continues rising rapidly across domains. Matches await for those dedicating themselves to mastering the mix of technical, analytical and communication abilities producing positive business outcomes with data!
