Data Analyst Online Courses & Certifications

The next revolution is already here, the data revolution, and it is a massive opportunity for those who know how to take advantage of it. As W. Edwards Deming, one of the leading thinkers in the field of management, said-

 

“Without data, you're just another person with an opinion”.

What is Data Analysis?

Data analysis is the process of using data to extract useful information, make decisions, or expand knowledge on various topics.

Data analysis involves subjecting the data to operations to obtain precise conclusions to help us achieve our objectives. These operations cannot be defined previously since the data collection may reveal specific difficulties.

Many industries use data analysis to draw conclusions and decide actions to implement. It is worth mentioning that science also uses data analysis to verify or discard existing theories or models.

What is a Data Analyst?

A data analyst is responsible for collecting, processing and analysing data to extract meaningful insights and support organisational decision-making. Data analysts are crucial in helping businesses and organisations make informed choices, optimise processes, and solve problems.

Top Data Analyst Designations and Skills

Top Data Analyst Designations	Skills
Data Analyst	Data Visualization, Data Cleaning, Statistical programming languages, like R or Python, Probability and Statistics, Statistical visualization, SQL and NoSQL, Machine Learning, MS Excel, SQL, Databases, Artificial Intelligence, Data management, Econometrics, Project Management, Negotiation, etc.
Data Architect	Applied Mathematics and Statistics, Data Visualization, Data Migration, Data Modeling, Relational Database Management System, DBMS software, including SQL Server, Database and Cloud Computing Design, Data Architecture and Data Lakes, Predictive Modeling, NLP and Text Analysis, Machine Learning, Programming Languages Python, C/C++, Java, Perl, etc.
Business Analyst	Statistical analysis software (e.g. R, SAS, SPSS, or STATA), SQL databases and database querying languages, Microsoft Excel, Creation of Reports and Dashboards, Programming skills such as Python and R, Survey/query software, Business intelligence, and reporting software, Sound knowledge of Tableau, QlikView, and Power BI, Data mining, Data visualization, Database design, Analytical and Critical Thinking, Communication and Interpersonal Skills, Decision-Making, etc.

 

Must Read - How to Become a Data Analyst from Scratch?

 

Tools and Frameworks for Data Analysts

Listed below are the important tools and frameworks used by data analysts -

Business Intelligence Tools	SAP Business Objects  Datapine  MicroStrategy  SAS Business Intelligence  Yellowfin BI  Zoho Analytics QlikSense Dundas BI Sisense
Statistical Analysis Tools	SPSS (IBM)  R (R Foundation for Statistical Computing)  MATLAB (The Mathworks)  Microsoft Excel  SAS (Statistical Analysis Software) GraphPad Prism Minitab Stat Graphics XLSTAT Qlik Sense
SQL Consoles	Aqua Data Studio Apache Zeppelin Cluvio  SQL Sentry Databricks SQL Analytics IBM Db2 Big SQL Microsoft SQL Server Singular Teradata Vantage Advanced SQL Engine Valentina
General-Purpose Programming Languages	Python SQL R Java Scala
Predictive Analytics Tools	H2O Driverless AI  BM Watson Studio  Microsoft Azure Machine Learning  RapidMiner Studio IBM SPSS Statistics SAS Advanced Analytics SAP Predictive Analytics  TIBCO Statistica
Data Modeling Tools	Draw.io  Lucidchart  SQuirreL SQL Client  MySQL Workbench  Amundsen  erwin Data Modeler  ER/Studio  InfoSphere  DbSchema Pro  Archi SQL Database Modeler Oracle SQL Developer Data Modeler    PgModeler
ETL Tools	Talend Informatica AWS Glue Stitch Pentaho Microsoft Azure Matillion Blendo Apache Spark SAS Amazon Redshift Tableau Apache Kafka
Automation Tools	Tableau RapidMiner KNIME Qlik Zoho Corporation Informatica Pentaho Spotfire HubSpot OpenRefine Datagaps Inc. Weka Tableau Public Data warehouse Orange Activebatch Amazon Redshift Teradata
Spreadsheet Applications	Microsoft Excel Tableau Jupyter Notebook Apache Spark SAS Microsoft Power BI Apple Numbers Google Sheets Quip EtherCalc Zoho Sheets LibreOffice Apache OpenOffice Calc Smartsheet Airtable Stackby SeaTable

 

Top Data Analytics Courses

Getting into the world of data can be a decision that helps you boost your career and significantly increase productivity at work. The more time and energy you spend studying the field, the more opportunities you have at your perusal. To help you learn essential data analytics skills, we have handpicked the top data analytics courses from the best e-learning platforms.

 

Introduction to Data Analytics for Business on Coursera (Duration – 11 hours)

Level – Beginner

Course Description 

The course covers the essentials of data analytics practices in the business world. It explores key areas of data analytical process, data creation, storage and access processes, and its usage at organizational levels. You will also learn about advanced investigative and computational methods.

Course Content 

1. Data and Analysis in the Real World
2. Analytical Tools
3. Data Extraction Using SQL
4. Real World Analytical Organizations

 

Business Metrics for Data-Driven Companies by Duke University on Coursera (Duration – 9 hours)

Level – Beginner

Course Description 

The course will help you learn best practices of using data analytics to recognize the most critical business metrics and distinguish them using data.

Course Content

1. Introducing Business Metrics
2. Working in the Business Data Analytics Marketplace
3. Going Deeper into Business Metrics
4. Applying Business Metrics to a Business Case Study

 

Data-driven Decision Making by PwC on Coursera (Duration – 9 hours)

Level – Beginner

Course Description 

In this course, you will get an introduction to data analytics and its role in business decisions, the basics of big data and its applications, and tools and techniques used in data analysis. You will also get to work in a simulated business setting to have hands-on experience in data analytics.

Course Content

1. Introduction to Data Analytics
2. Technology and types of data
3. Data analysis techniques and tools
4. Data-driven decision-making project

 

Big Data and Education by the University of Pennsylvania on edX (Duration – 8 weeks)

Level – Beginner

Course Description 

With this course, you will learn different data analytics methodologies in educational data mining, learning analytics, learning-at-scale, student modelling, and artificial intelligence communities. You will learn about applying methods using Python’s built-in machine learning library, SciKit-learn, using standard tools such as RapidMiner, and answering practical educational questions.

Course Content

1. Week 1 – Prediction Modeling
2. Week 2 – Model Goodness and Validation
3. Week 3 – Behavior Detection and Feature Engineering
4. Week 4 – Knowledge Inference
5. Week 5 – Relationship Mining
6. Week 6 – Visualization
7. Week 7 – Structure Discovery 
8. Week 8 – Discovery with Models

Applying Data Analytics in Marketing by the University of Illinois at Urbana-Champaign on Coursera (Duration – 20 hours)

Level – Intermediate

Course Description 

Applying Data Analytics in Marketing covers the basics of business analytics in the digital space. Designed for businesses and managers, the course explores the application of data analytics in the real world. You will learn to identify the ideal analytic tools, and understand valid and reliable ways to collect, analyse, visualise data, and utilise data in decision-making.

Course Content

Course Introduction – overview of marketing analytics; identifying customer satisfaction

Module 2 – Process of A/B testing, design of experiments, data analysis, hypothesis testing, and Analysis of Variance (ANOVA) 

Module 3 – Binary Outcome model using Logit function; Multidimensional Scaling (MDS)

Module 4 – Conjoint Analysis; Examples of the analysis in R

 

Data Analytics in Health – From Basics to Business on edX (Duration – 4 weeks)

Level – Intermediate

Course Description 

The course sheds light on the usage of big data in health care through novel data analytics-based solutions, leading to better diagnosis, care, and cure. It covers different real-world approaches using data analytics and explores entrepreneurial opportunities to help develop a business plan.

Course Content

 

Week 1: Module 1: Diabetes

 

Health data expenditure, machine learning, data transformation, deriving patterns, opportunities.

 

Week 2: Module 2: PCR Analysis

 

Introduction to PCR, data mining, competitive analysis, and industry analysis.

 

Week 3: Module 3: Genomic Data Analysis

 

Data sharing, data reliability, association rules, market research, marketing, solution optimisation.

 

Week 4: Module 4: Diagnostic Model Research

 

Workflow, data missing values, density maps, business modelling, requirements and planning, investment needs.

 

Integrated Program in Business Analytics (IPBA) on Jigsaw Academy (Duration – 10 months)

Level – Advanced

Course Description 

IIBA is a comprehensive course that covers Data Science, Statistical Modeling, Business Analytics, Visualization, Big Data, and Machine Learning. You will get to explore different tools and techniques involved in data extraction, data manipulation with SQL, data manipulation and processing with Python, and data visualisation with Tableau, among others. 

Course Requisite 

A valid GMAT / CAT / GRE / Jigsaw Administered Test score.

Course Content

1. Data Handling and Extraction with SQL and Python
2. Data Visualization with Tableau and Python
3. Statistical Data Analysis with R, Excel, and Python
4. Feature Engineering with Python for Structured and Unstructured Data Types
5. Predictive Statistical Modeling Algorithms
6. Machine Learning and Deep Learning Models on Text and Images
7. Implementing Algorithms at Scale with Big Data Systems
8. Generating Business Values and Effective Storytelling with Tableau

 

Prerequisites for Taking Online Data Analyst Courses

The prerequisites for learning a data analyst course online can vary depending on the specific course and institution offering the program. However, here are some common prerequisites to consider before taking data analysis courses - 

Educational Background

1. Most data analysis courses do not require a specific degree, but having a bachelor's degree in a related field, such as mathematics, statistics, computer science, economics, or social science, can be advantageous.
2. High school-level mathematics and statistics knowledge is typically essential, as data analysis often involves mathematical concepts and statistical techniques.

Computer Skills

1. You should have basic computer skills and be comfortable using a computer, including familiarity with operating systems (e.g., Windows, macOS) and standard software applications.
2. Proficiency in Microsoft Excel is often required, as it is a fundamental tool for data analysis.

Programming Skills (Recommended)

1. While not always mandatory, having some programming knowledge can be beneficial. Courses in data analysis often use programming languages like Python or R for data manipulation and analysis.
2. If you are unfamiliar with programming, consider introductory programming courses or tutorials before starting a data analyst course.

Statistics Knowledge (Recommended)

1. A basic understanding of statistics is essential for data analysis. Concepts like probability, hypothesis testing, and descriptive statistics are commonly used in the field.
2. If you lack a background in statistics, you should take a statistics course or review relevant materials before enrolling in a data analyst program.

Data Visualization (Recommended)

1. Familiarity with data visualisation tools and techniques is valuable for conveying insights from data. You can explore tools like Tableau, Power BI, or open-source alternatives.
2. Some online data analyst courses may include data visualisation components, but having prior exposure is helpful.

Language Proficiency: If the course is offered in a language other than your native language, you should have sufficient proficiency to understand course materials, lectures, and assignments.

Tips on Choosing the Best Online Data Analyst Course

Tip	Description
Define Your Goals	Determine your career objectives and specific skills you want to acquire as a data analyst.
Accreditation	Ensure the course or institution is accredited and recognised in data analysis.
Course Content	Review the course curriculum to ensure it covers relevant topics like data manipulation, statistics, and data visualisation.
Instructor Qualifications	Check the qualifications and experience of the course instructors.
Flexibility	Assess the course's flexibility in terms of scheduling and pacing to fit your current commitments and learning style.
Prerequisites	Verify that you meet the course prerequisites or identify any preparatory courses you may need to take.
Student Support	Investigate the availability of student support services, such as mentorship, forums, and technical assistance.
Hands-on Projects	Look for courses that include hands-on projects and real-world datasets to apply what you learn.
Reviews and Recommendations	Review reviews and seek past student recommendations to gauge the course's quality and effectiveness.
Cost and Financial Aid	Consider the course fees, payment options, and availability of financial aid or scholarships if applicable.

Potential Career Paths after Taking Data Analyst Courses

Listed below are the potential job roles and salaries after completing data analyst courses.

Job Role	Salary
Data Analyst	Rs. 1.8 - 11.7 Lakh
Business Analyst	Rs. 2.7 - 15.4 Lakh
Financial Analyst	Rs. 2 - 11.5 Lakh
Market Analyst	Rs. 2 - 13.9 Lakh
Data Engineer	Rs. 3.5 - 21.0 Lakhs
Data Architect	Rs. 14.3 - 50.0 Lakhs
Business Intelligence Developer	Rs. 3.0 - 16.0 Lakhs
Data Scientist	Rs. 3.9 - 28.0 Lakhs
Quantitative Analyst	Rs. 2.6 - 42.0 Lakhs
Operations Research Analyst	Rs. 2.6 - 15 Lakh

 

How to Succeed in a Data Analyst Career?

Here are some tips to succeed on your journey to mastery in data analytics.

Mastery of Fundamental Analytics Tools: MS Excel and Power BI 

Before entering the field of data analysis, you must familiarize yourself with the essential tools. Power BI and MS Excel are two fundamental pillars in this world. Power BI offers cutting-edge data visualization, while Excel is a versatile tool for manipulating and analyzing data. Ensure you have a solid grasp of these tools before diving into more complex projects.

Deep Understanding of Data Sources

Data analysts don't just work with data; they understand data sources. Before you begin your career, spend time understanding the different data sources you will be working with. Whether it's SQL databases, CSV files, or web APIs, knowing the intricacies of each source will give you a significant advantage.

Sharpen Your Communication Skills 

Data analysis isn't just about numbers; it also involves telling stories with those numbers. Develop practical communication skills to convey your findings clearly and persuasively. Translating complex data into understandable messages will be invaluable in your career.

Keeping you up to date with industry trends

The world of data analytics is constantly evolving. Stay on top of the latest trends, tools, and techniques by subscribing to blogs, participating in online communities, and attending conferences. Constantly updating yourself will ensure you are always on top of your game.

Develop Problem-Solving Skills

Data analysis often involves tackling complex challenges. Develop strong problem-solving skills to tackle unexpected situations. Thinking with an analytical mindset will enable you to overcome obstacles and find practical solutions.

Get Certified

As you advance in your career, consider earning official Microsoft Power BI and Excel certifications. These certifications validate your skills and provide official recognition that can raise your profile in the competitive world of data analysis. Reputable companies often look for professional certificates to guarantee competence and up-to-date knowledge.

Technical Skills: Mastery of SQL, Python and R

In addition to Power BI and MS Excel, it is essential to acquire skills in programming languages ​​such as SQL, Python, and R. These languages ​​will allow you to perform more advanced analysis, manipulate large data sets, and automate processes. Working with multiple tools will expand your versatility as a data analyst.

Build Strong Professional Relationships

Success in data analysis depends not only on your technical skills but also on your ability to connect with other professionals. Participate in industry events, join online groups, and build strong professional relationships. Networking will provide you with opportunities for learning and collaboration and open doors to new career opportunities.

Have a Grasp of Soft Skills

Besides technical skills, soft skills play a crucial role in success as a data analyst. These include developing effective communication, teamwork, critical thinking, and problem-solving skills. Empathy and translating technical results into understandable terms are essential when interacting with colleagues and stakeholders.

Conclusion

 

Data analysis has proved to be an essential component of the decision-making process for businesses. Irrespective of the field you are working in, be it marketing, tech, product, finance, design, or any other, your analytical capability will help propel your career in a meteoric way. The demand for data analysts is on the rise, and soon, they will be an integral part of every organisation and everything we do on a day-to-day basis. We hope that this write-up will be useful to you and will help you to strengthen your decision to take up data analyst courses.