Data Analytics is expanding on a large scale as a lot of businesses are using data to make informed decisions. Formal education in mathematics or statistics can be a plus, that being said learning skills required for data analytics can help to land a dream job in the field. Skill development has advanced to new heights with different trajectories as compared to times bygone. Can Data Analytics be self-taught? Yes, it can be! With flocks of content & information available, self-learning is accessible. A detailed and keen outlook is required to learn and develop new skills.

CAN DATA ANALYTICS BE SELF-TAUGHT

In this article, let’s explore the basics of Data analytics, and the skills required to become a self-taught data analyst with an understanding of resources to help in the process.

Let’s Decode Data Analytics, to Reach the Answer of the Question, Can Data Analytics Be Self-taught?

Data Analytics is the art of working with data to make growth decisions for business. We live in a data-driven time, and a small piece of information can be sufficient to understand the requirements or trends of the consumers.

Let us understand it with a simple example of day-to-day life. When a patient visits the doctor, the healthcare provider questions regarding discomfort or symptoms the patient is having. On understanding the information provided by the patient, the doctor diagnoses the disease and proceeds with the treatment.

So the doctor collected data, then structured it to his requirement, further extracted information about the diseases, and then prescribed the medicine.

Collecting various forms of data, modification & structuring of required information from data gathered. Extracting information & understanding consumer trends to predict future goal-driven decisions with facts-based information is the process of Data Analytics.

Moving ahead, in the quest of finding an answer to the question, Can Data Analytics be self-taught? Let’s understand four categories of Data Analytics.

Descriptive Analytics

Descriptive analytics works with data from the past, meaning things that have already occurred, and figures out what has happened. Businesses need insight into moving towards growth.

Descriptive analytics lays the first step by analyzing previous years’ sales, promotional strategies that help to increase sales, and ideas that did not work according to market trends.

Data aggression & Data Mining are techniques used in Descriptive analytics. Data aggregation helps to summarise the data in report form. Data mining is a process to understand the course of the market from the information available.

Diagnostic Analytics

A step ahead of Descriptive Analytics, Diagnostic Analytics digs deeper to find out reasons for the status of a business. Decisions that helped to boost sales or which helped to hinder growth are figured through this process. An X-ray of a human body part can be a good example, it shows what happened and the reason behind the fracture. Diagnostic Analytics helps in predicting future goals.

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Predictive Analytics

Predictive Analytics, as the name is all about prediction. Data is used to forecast future outcomes for businesses. A campaign, rooted in the sales in previous years may achieve the same goals in the future or not, if a product needs changes depending on the market, are some of the information gathered through predictive analysis.

Weather forecasts and Insurance policies are based on projections made by data from the past & present. Predictive Analytics is a key performance indicator for these two businesses.

Prescriptive Analytics

A doctor’s prescription comes only after the description of the disease from the patient, further diagnosing their symptoms and predicting a disease based on data received. Prescriptive Analytics is the amalgamation of the previous three types of Data Analytics. It sets a pathway for businesses to attain targets.

A customer who bought diapers will see various baby products or new mom care products in suggestions from E-commerce websites. Data is studied, and respective suggestions are made to the customers. Prescriptive Analytics is a data-driven approach to reaching targets.

Skill Sets Required to Self-learn Data Analytics.

Data Analytics requires both soft and technical skills. Learning these skill sets will solve the query, Can Data analytics be self-taught?

Skills That Work as an Asset for Data Analytics.

Data Cleaning

Data cleaning is a process of organizing heaps of data for structural decisions. Data cleaning is a crucial skill as it lays the foundation for the overall operation of data analytics. Uncleaned data can give misguided information that may undesired results.

Data Cleaning starts with importing data and looking for inaccuracies & duplicate data to polish up the data. Once improvement is done, data gaps are filled by finding missing data. An Outliers check is done at this stage, and after standardization, data is ready to be used for analytics.

Data Visualization

The human brain works better with pictorial formats of information. Humans understand and remember it better than numbers or words. Data visualization is visualizing & presenting the data in the form of charts, maps, and graphs.

The use of data visualization helps in the presentation of ongoing trends, marketing techniques, or data in a graphical format, making it easy for other professionals to grasp.

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Critical Thinking –

A coconut is full of layers that have to be removed to reach the edible part. Data is also layered with information. Critical thinking in data analytics helps them to find trends, patterns, factors for growing sales, and consumer thought processes through layers. Analyzing data requires out-of-the-box thinking, to generate links between various information.

Communication & Presentation skills –

The data analytics role requires a lot of communication with people from various understandings and streams. While working with colleagues, communication will be a factor in giving an overview of your analytics. Often, good communication skills come in handy when explaining or listening to non-technical background coworkers.

The presentation of data makes people understand the motive that data analytics has visualized. It is possible with good presentation skills. Continuous practice and focus on details can help to enhance communication & presentation skills. To present data effectively, both visually and verbally is essential for data analytics.

Can Data Analytics Be Self-taught? Yes! Mastering These Technical Skills is a Solution to the Question.

Excel or Google Sheets

A spreadsheet comes to mind on hearing the word Excel, a lot more detailing is involved in it. Excel or Google spreadsheet are the basics of starting to understand complex data. It’s great for newcomers to analyze a small database.

VBA (Visual Basics for Application) is used to write macros in Excel, which is a great tool for data analytics working in startups with relatively lessdata. All the data analytics courses cover Excel or Google Sheets. Free tutorials are available to learn Excel in detail.

 

SQL

SQL or Structured Query Language is a standard programming language to connect with databases. It’s known to be very advanced from Excel or Google Sheets. SQL is a must-have tool in the skill set of data analytics.

SQL allows data analytics to search through data and find answers to queries, discover patterns of consumer requirements, and manipulate data. It is an easy-to-learn language with a huge number of users to help newcomers by joining communities.

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R and Python

R and Python are advanced statistical languages known for handling and assessing big data sets. One of the two languages, either R or Python, in the skillset of a data analyst is a huge asset.

Python’s huge set of libraries like Pandas, Numpy, Matplotlib, and more, help data analytics to process and visualize data quickly and effectively. Data analytics can save time by using these to collect, clean, analyze, and visualize data.

Tableau and Power BI

Tableau

Tableau is a visual-making software that helps data analytics to visualize data in a more presentable form of charts and graphs. Tableau can engage easily with SQL queries. Technical skills are not required to use Tableau.

Power BI 

Power BI stands for Power Business Intelligence. As the name suggests, Power BI organizes, analyses, and gives results for business growth with raw data. It is widely used for data analytics as it does not require any coding or technical knowledge.

Machine Learning

Machine learning is a part of artificial intelligence that allows computers to learn from data and make predictions. Knowing machine learning can enhance the chances of landing good jobs in the data analyst field. Few sections of machine learning to dig a bit deeper into.

Supervised Learning 

Supervised learning is machine learning known to use labeled datasets and it requires human supervision. These datasets categorize data & project results.

Unsupervised Learning

Unsupervised learning is machine learning that works with unlabeled datasets. Human intervention is not provided. Patterns or trends are figured out from unlabeled data provided.

 
NLP Natural Language Processing

NLP, a supplementary branch of machine learning, acts as a bond between computer and human language. NLP assists computers in decoding and processing human language to perform repetitive tasks.

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How Can Data Analytics Be Self-taught?

Yes!  Let’s See the Steps to Become a Self-learned Data Analyst.

 

1. Realistic Goals

Set realistic goals for yourself. Learning a new skill with no background in the field may get overwhelming. The key to keep going is to take one step at a time. Make a plan and targets for each day, achieving those will boost confidence.

 

2. Continuous Learning

Data analysis is a field that keeps developing. Regular learning is required to stay well-informed about current trends and programming languages used in the field.

 

3. Have a Roadmap

Make a step-by-step plan for self-learning. It helps to stay motivated and on the right course of action. Without a plan, it may get clueless and difficult to explore.

 

4. Choose a Programming Language

Excel is an entry to learning to decode the world of data. Python, SQL, and R are the most common and popular programming languages. There are many others as well. Mastering one tool at a time and keeping it up to date with ongoing trends and upgradation will take you to the answer of the question, “Can Data Analytics be self-taught?’’.

 

5. Understanding the Data Analytics & Resources to Self-learn It.

Plenty of information is available online explaining data analysis. Books, online courses on many tech platforms, and YouTube channels help to understand and develop skills required for the field. Reading books enhances knowledge.

 
Specific books for SQL, Python, Excel, and R are available. Data Analytics online courses are available to give guidance and knowledge. It is necessary to find the resources that fit your pace of growth and needs, from the abundance of options available such as books, blogs, and online courses. These resources will give you a roadway to the quest of “Can data analytics be self-taught”

 

6. Practice with Real Data

Many websites like Reddit, Kaggle, Google Dataset Search, and Datacamp are places to provide a variety of datasets to work on. Working on Real Data projects gives hands-on experience.

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7. Join Online Communities

Connect with like-minded people and join communities. Networking helps a lot in learning new skills. Kaggle is a community that regularly hosts competitions with datasets.

 

8. Build a Portfolio

A portfolio is a showcase of your skill set. The resume alone may not be enough to convince. Hiring managers, Clients, and Interviewers can assess work done through a portfolio.

 

Can Data Analytics Be Self-taught? Of Course!!

These Books Are Answers to Learning Skills to Become a Self-learned Data Analytic.

Data Analytics Made Accessible by Anil Maheshwari Data Analytics can be overwhelming to start learning. Data Analytics Made Accessible caters to the need of beginners to take one step at a time.

Being written in a conversational tone makes it relatable to readers. Every chapter starts with examples of datasets from real-world examples, moving to concepts and various case studies as exercises to apply those concepts.

The book covers Data Mining, Big Data, Artificial Neural Networks, Data warehousing, Business Intelligence, and Artificial Intelligence & Decision Trees. The book has been updated from time to time, since its publication in 2014. The data mining tutorial in the appendix assists readers with their first predictions.

Numsense! Data science for the layman (no math added) by Annalyn Ng and Kenneth Soo.

As the name suggests, the book is for laymen without math, statistics, coding, or anything technical. The Numsense is filled with data visuals, making it effortless for beginners to grasp, especially from a non-statistics, math, or coding background.

This informative book is perfect to start the journey of Data Analytics. The chapters explain various algorithms and how they work with real-world examples. Terminology of Data Analytic terms, outlines, and guidebook containing positives & negatives of all algorithms are in the book.

Practical SQL: A Beginner’s Guide to Storytelling with Data by Anthony Debross

SQL is a primary tool of data analytics. Practical SQL is the answer to learning this primary tool. A comprehensive read with the usage of real-world datasets to make it intriguing for readers. New datasets with questions to solve in exercises.

Author Anthony Debross is an award-winning journalist who works as a data editor for the Wall Street Journal. He has made Practical SQL a storytelling tool through data optimization. The topics covered are time, GIS, functions, and how to understand more advanced queries.

Math & advanced statistical functions, picking out data errors, data cleaning, and finding patterns in data by filtering & sorting are key points to learn from Practical SQL.

Python for Everybody: Exploring Data in Python 3 by Dr. Charles Severance

Python for Everybody is a book for students to take a first dip in the ocean of software development and programming. Python is broadly used to solve problems involving big datasets that spreadsheets cannot handle.

Python is a user-friendly language available for free on Macintosh, Windows, or Linux computers. The self-explanatory book filled with examples & information, useful for projects, and free as an EBook is a reply to the question, “Can Data analytics be self-taught?”

The Hundred-Page Machine Learning Book by Andriy Burkov

The Hundred Page is a to-the-point and organized book for new learners wanting to explore the world of Machine learning. Topics included are supervised & unsupervised learning, neural networks, cluster analysis, missing values & categorical variables. Machine learning is explained in a nutshell by the author.

It comes accompanied by a continuously upgraded wiki, which helps in follow-up references and expands information on topics.

 
Naked Statistics: Stripping the Dread from the Data by Charles    Wheelan.

Did you not concentrate on statistics class in school or college? No worries, Charles Wheelan has you covered. The book explains concepts of inference, probability, central limit theorem, mean, average, correlation, and regression analysis with examples. Examples are the financial crisis of 2008 and the Monty Hall problem.

The author has also focused on how statistics can give incorrect results if not used appropriately. Results can be dangerous or useless if data is not assembled accurately.

Big Data: a Revolution That Will Transform How We Live, Work and Think by Viktor Mayer-schönberger and Kenneth Cukier

Big Data, the book is all information about the big data that surrounds us every moment. The book is in easy and understandable language. Pictures, videos & texts generate data, and applications on phones, laptops, e-commerce websites, banking transactions, and a lot more are generating loads & loads of data.

The book talks about, analyzing and studying big data sets. Finding results working on big sets. The book covers various aspects of data, proving to be a good read for newbies.

 Online Courses Available for Data Analytics- Can Data Analytics Be Self-Taught

Data Analytics is a booming field with high salaries and expanding employment opportunities. Online learning platforms are effective means to start exploring the data analytics field with books. Many self-learning courses are available, whereas many provide live sessions from experts.

Some of the Key Features of Online Courses-

  • Learning at your own pace with flexibility.
  • Recorded classes to refer to them infinite times.
  • Hands-on experience projects.
  • Network of your own to share insights.
  • Portfolio building.
  • Expert’s views on assignments completed.
  • Certificates on completion of the course. 

 

Frequently Asked Questions

Q. Can Data Analytics be self-taught?

Yes, data analytics can be self-learned. Some resources can be selected at your convenience and pace. You need to figure out a perfect resource like a book or a course.

Q. Do companies hire self-taught Data Analysts?

Yes, learning new skills is no longer limited to formal education, and yes, companies do hire self-learning Data Analysts. Portfolio development and networking will be a great help in this regard. Keep yourself updated with the latest trends and technologies and prepare for interviews.

Q. What skills are required to become a Data Analytics?

Data Analytics requires both soft and technical skills to flourish in the field. Technical skills may vary from company to company. Here is a list of   skills required-

  • Data Cleaning
  • Data Visualization
  • Critical thinking.
  • Communication
  • Presentation Skills.

Technical Skills

  • Excel & Google Sheets.
  • SQL
  • R and Python.
  • Tableau & Power BI
  • Machine Learning

Q.  Are there any resources that can help me self -learn Data Analytics?

There are many books written by experts from the data analytics field and online courses with live classes are also available.

  • Data Analytics Made Accessible by Anil Maheshwari
  • Numsense! Data science for the layman (no math added) by Annalyn Ng and Kenneth Soo.
  • Practical SQL: A Beginner’s Guide to Storytelling with Data by Anthony Debross
  • Python for Everybody: Exploring Data in Python 3 by Dr. Charles Severance
  • The Hundred-Page Machine Learning Book by Andriy Burkov
  • Naked Statistics: Stripping the Dread from the Data by Charles Wheelan
  • Big Data: A Revolution that will transform how We Live, Work And Think by Viktor Mayer-Schönberger and Kenneth Cukier

IIM SKILLS provides a course named Data Analytics Course, with live sessions. This course contains learning various skills required for Data Analytics and 2 2-month internships after completion of the course with renowned companies.

Conclusion

In conclusion, ‘Can Data Analytics be self-taught?’ is not only a question of possibility but also about entering into a data-driven world filled with opportunities and growth. According to recent data from IBM, job openings in Data Analytics in 2030 are estimated to be 49.2 million, representing a growth of 15% per year from the year 2020 to 2030. Get on the journey, start exploring resources, and developing skills to reach the answer to the question, Can Data Analytics be self-taught?

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