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How to Start Learning Data Analytics – A Comprehensive Guide

We live in a world where the internet is no longer a luxury but a necessity and data is no more mere numbers but a golden pathway to stay one step ahead in this competitive world. The Internet has taken the world by storm and when you can learn to build a car from scratch, why not work on How to Start Learning Data Analytics on your own, and figure out a way? In this article, we focus on why you should think of stepping into this field, how could you become an Analyst and the benefits of Data Analytics.

How to Start Learning Data Analytics

Introduction to Data Analytics- Understanding the Fundamentals

How to Start Learning Data Analytics especially if you are from a non-technical background? If you have an analytical bend of mind, are interested in learning new technology, and mathematics and statistics are subjects that interest you, then it is easy for you to start learning Data Analytics on your own. The only prerequisite is that you are ready to learn new things and upgrade your knowledge constantly and are ready to evolve. To start your journey on “How to Start Learning Data Analytics”, first let’s focus on what are the fundamental topics falling under the purview of Data Analytics.

  • Data Collection: The methods for Data Collection can be classified under two categories, Primary and Secondary. Primary Data Collection is when the data is collected directly from the respondent which is known as primary data collection. The researcher has the advantage of altering the collection method that suits their purpose. The methods involved in this type of collection are surveys and questionnaires, interviews, study groups, and focus groups. Secondary Data Collection is when the information is collected from pre-existing data, then it is known as Secondary Data Collection Method. The sources to apply this method are published sources like newspapers, medical journals, surveys, statistic reports, government records, and data published for the public by individuals like expert opinions, and social organizations, this is a secondary method of data collection and past research studies.
  • Data Preprocessing and Cleaning:  The data when collected is in raw form. It is then cleaned, modified, and consolidated before it is subjected to analysis. This process is known as Preprocessing. The methods for processing are Data Cleaning in which errors like outliers, duplicate data, and missing values are identified, and removed with the help of methods like Binning method, regression, and clustering. Data Transformation is the second step and it is the process of categorizing the cleaned data into set formats for ease in Analyzing. The methods employed for this are Normalization, Standardization, Dicreditization, Concept Hierarchy generation, and Attribute Selection.

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The last step in preprocessing is Data Consolidation which is the process of reducing the data to have more focused data for analysis, but without losing any important information. The methods for Data Consolidation are Data Cube Aggregator, Attribute Subset Selection, Numerosity Reduction, and Dimensionality Reduction.

  • Data Exploration: This includes identifying patterns and trends which are relevant to the goals fixed by the company, before the final analysis is known as Data Exploration. The data which distorts the analysis is removed and the data which aids the final analysis is separated to be focused on only.
  • Data Analysis: It encompasses discovering trends and patterns in the data and arriving to figure out the relationship between them, so as to understand the negative or positive impact that events have had on the targeted activities of the company. There are mainly four types and Data Analytics methods. And they are Descriptive Analysis, Diagnostic Analysis, Predictive Analysis, and Prescriptive Analysis.
  • Data Visualization: The graphical representation of the analytics report with visual aids like graphs, pies, charts, Geospatial maps, infographics, and Dashboards is known as Data Visualization. The mainobjective of this method is to make the Data Analytics reports and findings comprehensible to non-technical people.
  • Data Modeling: This involves creating a blueprint for the software for data analysis based on the objective of the company is known as Data Modeling. The diagram identifies the specific data as the base, the text and symbols to identify that data, the data flow source, and the relationship between the variables. There are different types of data modeling applications, a few being generally used often are Star Schema, Hierarchial Database Model, Relational Model, Object-Oriented Model, Network Model, Entity Relationship Model, and Document Model.
  • Interpretation and Inferences: This involves interpreting the reasons of the relationship between the variables which either have created a bottleneck or been an aggregator of growth is known as Interpretation. The end goal of this process is to assist the company to make informed business decisions.

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Setting Clear Goals

Before you embark on the journey of gathering information on “How to Start Learning Data Analytics”, first the answer to the question “Why do you want to learn Data Analytics” should be clear. Here are a few reasons to assist you in your goal-defining decisions:

  • Job Diversity: Data Analytics is being used in every possible business sector now, be it healthcare, telecommunications, retail, sales, marketing, entertainment, manufacturing, and others. A Data Analyst gets the option of choosing the industry which entices him more.
  • Growth Opportunity: Data Analytics is a constantly evolving and growing sector with the latest technologies being introduced every other second day. Thus, a candidate gets to grow and constantly evolve by being constantly updated about the new technologies being introduced.
  • Enhances Problem-Solving Skills: Data Analytics requires the professional to be good in analytical skills as they have to infer solutions from the data presented. Data Analytics has to be able to think out of the box to be able to see patterns and trends which influence decisions in positive different ways.
  • High Remuneration and Demand: The world is at a great speed getting caught in the data revolution. Most companies are relying on data analysis to make informed decisions and gain an edge in today’s competitive world. Due to these reasons, the demand for Data Analysts is also growing. As Data Analysts have the responsibility to assist in critical business solutions, their salary packages are also higher.
  • Interpersonal and Communication Skills: Data Analysts don’t just have to sit behind a screen and take decisions, but they have to interact with different types of people to convey these decisions too. This helps them in building their interpersonal relationship skills, leadership skills, and communication skills, as they have to make presentations in front of the members of the company and clients too.

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Explore Data Analytics Tools

The main responsibility of a Data Analyst is to gather data and analyze it. The second step in “How to Start Learning Data Analytics” on your own is to get acquainted with the tools which are used in Data Analytics. Some tools are free and some are paid, although the paid versions have a free trial period, which one can learn. Here is a list of the most preferred Data Analytics Tools: 

  • Excel: This is a part of Microsoft Office Suite. It is used for data collection and statistical analysis. The spreadsheet software allows the analyst to clean data and also the pivot function helps in reporting. It is helpful in data wrangling.
  • R: This is free software. It is helpful in data modeling, data visualization, and data mining. It has thousands of free codes in the library known as CRAN.
  • Python: This is a free open source software. It has thousands of free libraries. It is mainly used for data scraping, and analysis and has other usages also.
  • Tableau: This is widely used in almost all companies for visualization, analysis, and data interpretation. Data Analysts can build interactive dashboards on this software. It is excellent for handling Big Data and huge data sources.
  • QlikView: This is another good Data Analytics tool that is also helpful in data integration, data visualization, data literacy, and business intelligence. It is excellent for getting information on data like identifying patterns and trends.
  • Power BI: This software has been adopted by all companies due to its excellent usage in data visualization, predictive analysis, making live dashboards, and connecting data sources.
  • SAS: This is paid commercial software. It is helpful in predictive analysis, data management, data mining, generating reports, and profiling of customers.
  • KNIME: This is a shortform for Konstanze Information Miner. It is free software, excellent for gathering data, data integration, data mining, customer profiling and analysis, machine learning, and business intelligence.
  • Apache Spark: Data Analysts use it when they have huge unstructured data, as it can process Big Data without any setbacks. It is also helpful in machine learning.
  • SQL: This is another software in the list of mostly used in companies. It is used when the data is structured. It finds its use in Data Modeling, data analysis, and collection

Explore Online Learning Platforms

The next best step on your journey to “How to Start Learning Data Analytics” is enrolling yourself in a good Data Analytics Course. The following are strong reasons for joining a well renowned institute of good remote:

  • Syllabus: The course syllabus of any institute is all-inclusive, which means, that all the topics to be learned can be accessed under one roof. The course is designed by industry experts, thus, one is assured that the latest technologies would be taught.
  • Instructors: The instructors and mentors are experienced industry experts who train the students on the latest practices in the industry, guide students on all technical problems, and help in understanding the latest jargon too.
  • Capstone Project and Assignments: A Capstone Project helps students to understand the live world situations in a firm and work on the same. This in turn helps students gain experience and build a strong profile. Assignments and projects during the training also give exposure to the students to cases where they can work in a guided environment.
  • Networking: The advantage of enrolling in a course is being a part of the student alumni and getting access to the network of peers who are already well placed in the industry.
  • Continuous Learning: The reading material and videos from the institutes are accessible for a lifetime to the students. 
  • Practice with Real Data

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The best practice How to Start Learning Data Analytics on your own is to practice with different data sets. Here is a list of a few sites from which one can download data sets and practice : 

  • FiveThirtyEight: This is a news and sports website, but it is interactive and has a variety of data sets. It is good for people practicing Data Visualization.
  • Jeremy Singer-Vine: Collection of multiple data sets from different sources.
  • Quandl: Collection of data sets on economics and finance
  • Academic Torrents: Collection of data sets from the scientific community
  • Google Public Datasets: Collection of multiple data sets from all possible public sources.
  • Data.Gov: Collection of databases released by the US Government, like budgets, climate, schools, climate, etc.
  • GitHub: Their Awesome-Public-Datasets have a collection of published data sets available to all.
  • UCI Machine Learning Repository: It is hosted by the University of California, especially for budding machine learners.
  • Socrata: The data sets are already cleaned and are in the government, education, and business category.
  • Kaggle: The website has data sets from different fields for practice. It regularly hosts competitions in modeling and other areas.

Learn Data Visualization

The role and responsibility of a Data Analyst are not restricted to just being behind the computer and analyzing data sets. The most important role of a Data Analyst is the ability to present their findings in a comprehensible manner. The sooner one works on their communication skills, the better chances of landing a good job. The next important step in “How to Start Learning Data Analytics” on your own is to start practicing Data Visualization from the beginning. Here are a few steps to assist you in this journey: 

Selecting Tools: There are a number of advanced Data Visualization Tools available to practice. Check the ones that are latest and the ones which are widely used. Here is a list of some of the latest tools used in 2023: 

  • Power BI
  • Tableau
  • Dundas BI
  • JupyteR
  • Zoho Reports
  • Google Charts
  • Metaplotlib
  • RAW
  • IBM Watson
  • Poltly
  • Sisense

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Choose Data: There are multiple data sets that are cleaned and categorized and even raw data available on the internet. Start by choosing the data set which is convenient to understand. Eventually, start experimenting with all data sets.

Define Objective: Identify the trends, patterns, and relationships are you targeting on finding from the chosen data set.

Choose the Right Charts: All the Data Visualization Tools have a plethora of charts, graphs, and other presentation tools. Choose the right tool for presentation. Ensure it is not too colorful and too complicated. The tools should be able to convey the key message while being able to be easy and captivating for the audience.

Story Telling: Presentations should be to the point, precise, and yet interesting, a lot like storytelling to captivate the audience yet easily comprehendible. Build a storyline around the presentation and keep practicing.

Feedback & Reviews: Join data visualization communities and present and practice with them. This will help in getting unbiased feedback and tips for further improvements by experts. Data Visualization Society (www.datavisualizationsociety.org), Visme (www.visme.co), Tableau, and Observablehq (www.observablehq.com) are a few communities one can join.

Diversify: Start taking data sets from multiple sources and Big Data for practice. Practice different  Data Visualization methods for the same data set and keep experimenting with new sets.

Enroll in a Course: Another easier manner to practice, polish, and obtain new skill sets for you is to enroll in any of the renowned institutes specializing in Data Visualization.

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Join Data Analytics Community

There are many societies globally that are dedicated to Data Analytics enthusiasts. Being an aspiring candidate in the same field, the next important step keeping in the continuation of “How to Start Learning Data Analytics” is joining any Data Analyst community. Here is why one should join a community: 

  • Learning Opportunities: The communities have members ranging from students to experts with years of experience and in-depth knowledge about the subject. Participating in discussions or forums and even individual communication gives freshers and other professionals opportunities to learn new things.
  • Networking: The members and peer community of this group have experts from all industries at all varied positions. Interacting with them strengthens the opportunity to increase your networking.
  • Problem Solving: These communities hold discussions and open forums frequently which gives an opportunity to interact with them and take their opinions and assistance in any technical problems or learning problems one is faced with.
  • Competitions: These communities regularly hold datathons, competitions, hackathons, and other challenges. The participants are from all industries and all levels of expertise. This gives an excellent opportunity to work in teams or individually on projects taken from real-world industries. It encourages competitors to be creative and come up with varied solutions. Team competitions help in increasing one’s network, getting new ideas and different perspectives. Competitions polish the analytical and soft skills of the individual and help in building a strong portfolio.
  • Continuous Learning: Community discussions and forums help the fresh members to be updated about the latest trends, technologies, and tools being introduced and practiced, and the emerging tools and trends.

Enroll in Data Analytics Course

In the event of finding the above mentioned steps to “How to Start Learning Data Analytics” on your own, intimidating, it is best to invest in good Data Analytics Courses from renowned institutes. Here is a list of the top 10 Data Analytics Institutes, to be looked into further:

  • IIM Skills (www.iimskills.com)
  • Imarticus (www.imarticus.org)
  • IIT Kanpur (www.ifacet.iitk.ac.in)
  • IIM Calcutta (www.iimcal.ac.in)
  • Harvard Business School (www.exed.hbu.edu)
  • Google ( through Coursera)
  • Udemy (www.udemy.com)
  • Emeritus with IIM Kozhikode ( www.emeritus.org)
  • Goa Institute of Management (www.gim.ac.in)
  • Purdue University with IBM (www.purdeu.edu)

Conclusion

In conclusion, Data Analytics is a dynamic, and constantly evolving field in which new technologies keep getting introduced frequently. If you are looking for a career that is dynamic, growing and holds an important place in the company, then Data Analytics is the right choice. As it is clear in this article, your journey on “How to Start Learning Data Analytics” by yourself is not that complicated. An aspiring data analyst can start learning right from home. 

How to Start Learning Data Analytics, the right way has to begin with understanding the fundamentals of the subject and the internet is a treasure trove of information and material on the same. Understanding the tools and technologies can be done effectively either in an institute or with the help of books and resources available. Joining a data analyst community is a must as here one gets to network and exchange notes, and learn from peers and experts alike. They also help in gaining confidence and assist in tackling complex problems. Thus, set a goal and embark on this fulfilling journey of becoming a Data Analyst, and don’t forget persistence and practice is the key to being successful even on your own.

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FAQs

Q. How to Start Learning Data Analytics and why is it important?

The foundation of learning the basics of Data Analytics can start from home by learning about data, the important fundamentals of data analysis, the tools of data analysis, joining an online training course, and joining the communities online.

Data Analytics has been emerging as the most important tool for a company to make informed data-driven decisions, based on which they can improve profitability and remove bottlenecks from the company. To stay ahead in the competitive world all companies have started taking the assistance of data analysts, thus, data analysis is a very important role in a company.

Q. How to Start Learning Data Analytics even if I have no technical background?

If you have an analytical mindset and are from a finance or economics, or maths background then beginning to learn Data Analytics is not so difficult. The internet has multiple websites with information on the latest tools used in data analysis, and then most of the classes that are online have industry experts as trainers and mentors, who set a comprehensive, comprehendible course set easily understood by all. Then there are networks and groups that have a mixed group of beginners and experts, from whom one can constantly keep learning the latest technologies, upcoming trends, and solutions to technology difficulties.

Q. How to Start Learning Data Analytics applications and tools at home? 

The fundamentals of Data Analytics can be learned through multiple online classes. The tools of Data Analytics are covered in the course and plenty of information can be found on the internet. To practice with real data sets, websites like Google Public Datasets, GitHub, Kaggle, Socrata and many more have data sets from multiple sources. Data Visualization which is another important part of Data Analytics can also be practiced on data sets available on the websites. Joining any Data Analytics Communities is another good way to learn about new tools and technologies, enhancing creativity by participating in competitions held by them and being a part of the forums and discussion groups.

 

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Geetanjali Pantvaidya is a Post Graduate in MBA Marketing from Army Institue of Management Kolkatta. A Y2k batch pass out , She started her career with Caltiger.com which the country’s first free ISP. She has over 12 years experience in marketing working in the telecom industry, banking , insurance and the education industry. Hailing from an army family background, the love for travelling was deeply rooted in her veins since childhood, thus, her stint as a travel manager with Thomas Cook. She embarked on her journey as a content writer with a travel company.

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