Beginner's Guide to Data Analytics

Beginner's Guide to Data Analytics

The era for collecting and preserving data is more prevalent than ever. Hence, people need to understand, analyze and extract information in the right format, which is one of the critical components of many businesses and organizations. Data analytics was first introduced to the business market in the late 1960s. Still, the recent developments in fields like big data, cloud computing, and other software and hardware reforms have helped in the significant evolution of data analytics, so much so that it has now become an integral part of businesses and their decision-making strategies. It has also raised career growth opportunities.

What are data analytics?

Everything we do on the internet, like open an application, answer a survey, or even fill out a CAPTCHA, is collected as data by different organizations and businesses governing them. They form a colossal amount of data, collected every second, both on and off the internet. Have you ever wondered what happens to this data? 99.5% of this data remains unused, and only 0.5% is used for data analytics purposes. Among the extensive data that the world receives today, even 0.5% of data is colossal. It requires a systematic approach to maintain this data and put it to good use in business and other organizations.

Professionals in the field of data analytics are called data analysts, and their primary aim is to convert this raw data into meaningful use. Data analytics help businesses and organizations make informed decisions using this data, which can help them to grow and monitor their businesses. They can also predict their organization's future by analyzing the collected data metrics.

Type of data analytics

Since, as mentioned above, vast volumes of data are generated every second from different sources, there ought to be different ways to extract essential information for different purposes. However, while extracting the data, analysts generally focus on the four following methods:

Descriptive analytics

They answer the question – what happened?

The data collected using these methods generally answers the question of what happened in the system, which resulted in delivering this type of data. In addition, it provides a feasible way of converting the raw data into a digestible format. Descriptive data analysis procedures use data mining and aggregation procedures, which provide a detailed overview of past actions. This is one of the critical steps in understanding the in-depth analysis of specific procedures which will make or break a particular project in a business or organization. 

Diagnostic analytics

They answer the question – Why it happened?

One of the primary differences between descriptive and diagnostic analytics procedures is while the former provides an objective view of the happenings of the data, the latter provides a detailed overview of the situation and turn of events that might have led to the results extracted. Data analysts generally identify the outliners, understand the anomalies within the data, provide a theoretical approach on why it might have happened, and present a solution to rectify it or tweak it in ways beneficial for the business or organization.

Predictive analytics

They answer the question: What will happen in the future?

As the name suggests, predictive analytics uses the previous raw data, patterns, and trends to predict the outcome in the future and expect the likelihood of the occurrence of events. In this procedure, a data analyst will use variables and tools to gauge the likelihood of an event. They generally use sales figures, seasonality, and other pointers, which can help the organization make better decisions and boost their sales. Based on this data, all organization departments can tweak their strategies to perform better.

Prescriptive analytics

They answer the question – What is the course of action?

You can think of prescriptive analytics as a conclusion step to the collected raw data. By using the above two methods, analysts can understand what happened and why it happened, and using this method, they will be able to make a detailed, well-informed decision on what steps can be taken to make better judgments.

In layman's terms, using Google maps is one of the best examples of prescriptive analytics. When you enter the start and end destination, you will be provided with the best route to reach while considering the traffic situations, road closures, and other information to provide the shortest possible route. A user will use all the data provided by the application to make an informed decision for the next course of action.

Process of data analytics

The process of data analysis is usually done in the following five steps

Step 1: Defining the Question

In order to start the process of analyzing a data set, it is essential to define the problem statement or the objective of the analysis. One of the most central questions asked by analysts in this step is –'what business problem are we willing to solve?' This question will define the entire framework for analysis.

Step 2: Collection of data

Once the problem statement is defined, the entire objective for analysis can be framed, and strategies will be created to collect the appropriate amount of data. But, of course, this step also needs a thorough analysis of the kind of data needed, like quantitative (numeric), qualitative (descriptive), or customer surveys.

Step 3: Cleaning the data

Cleaning the data means eliminating incorrect information or human errors or is simply unrelated to the problem statement. Unfortunately, most organizations forget this step, which can hamper their analysis process. This process is also called scrubbing, and it involves removing unwanted data, eliminating outliners, duplicates, and errors, filling in for missing data, and providing a structure to the collected data. It is the most time-consuming of all the five steps.

Step 4: Analyzing the data

Once data is collected in the right format, analysts now enter the fun part of analyzing the collected data. But, first, they analyze the data using the four methods mentioned above, applying methodologies and different techniques to find a solution to the problem statement.

Step 5: Visualizing and sharing the findings from the data

By this point, the analysts have gathered data and have developed insights into the same. Next, they must understand the significance of the collected data and provide a viable solution based on their analysis to the key stakeholders. In this process, professionals generally analyze the collected data using tools like Microsoft Power B.I., Tableau, and others, which help generate results in graphs, dashboards, reports, or interactive visualizations. Analysts need to be transparent regarding the findings in this step so that the next team will be able to make an informed and apt decision.

Beginner's Guide to Data Analytics

How to become a data analyst?

Anyone with a relatable degree is capable of becoming a data analyst. It is also one of the best options for people to change their career paths, which involves a few hard and soft skills every analyst will need. If you're starting for a career change or wish to opt data analysis as a prospective career

Hard skills

  • A commendable knowledge of programming like Python
  • Demonstrable skill in query languages like SQL
  • Proficiency in business intelligence
  • Ability to manage different analytics and business intelligence software, linked by not limited to SAS, RapidMiner, Tableau
  • Solid understanding of the data analytics process
  • Ability to be able to solve queries using statistical and numerical skills

Soft Skills

  • Ability to communicate among different team members
  • Excellent collaborative skills
  • Ability to apply logical and systematic reasoning to problem statements
  • A problem-solving mindset

Data analytics involves extensive training and resources to be able to reach a higher ladder in the data career. However, it also does not require any formal training. Most companies hire fresher trainees and train them in a field required and well-suited for the company to meet their goals. If you're a fresher, you can consider workshops. For example, Tech I.S. has great resources for developers, boot camps, and other analytics programs that cover all the basic understanding and skills needed to become an expert in data analysis. You need to look for courses that offer:

  • Project-based curriculum
  • One-on-one mentoring 
  • Networking and job opportunities
  • Certification of completion
  • Job guarantees and exposure to different corporate processes

However, a recruiter might expect you to know the following to bag a position in data analysis if you're a professional looking for a job or career change.

  • Critical thinking: Data analytics is all about being able to find different methods and solutions to solve problem statements, depending on the type of data available. Hence, if you're able to adapt to critical thinking, you can approach the problem statement in different ways.
  • Data Wrangling: It is an important process of cleaning the raw, collected data for analysis and visualization, which involves resolving mistakes and filtering data to put them to good use. It is essential for anyone dealing with data to be able to segregate based on their quality and extract quality data.
  • Mathematical ability: You do not have to be a mathematician, but you should have the good mathematical knowledge to analyze more complex problems and find a viable solution for the probes. A seasoned professional in the field should have a good understanding of linear algebra, calculus, statistics, and probability, at the least.
  • Hypothesis formation and testing: The heart of data analysis is finding solutions to different questions and forming a hypothesis to analyze and provide a viable solution. Without a proper hypothesis, your problem statement analysis will have no clear direction. Hence no clear solution can be obtained.
  • Machine learning: Although it is not a compulsory topic for professional in the field, it is an added advantage as machine learning techniques helps in the use of algorithms and provide an ability for people to adapt to numbers and data. It will strengthen the concepts of automation, trading, risk management, as well as performance analysis.

Key Takeaway

Data analytics is a great way to boost your career opportunity and also provide a comprehensive approach to dealing with raw data and putting it to the best use for businesses in other organizations. Many professionals and freshers in the field are creating career prospects and earning lump sums in the field. Since it is a growing branch of dealing with data, it needs extensive business working knowledge and the scientific and mathematical ability to deal with data. Data analytics is also applied in different fields and forms the basis of training business decisions to improve product sales. Suppose you're a fresher looking forward to taking up data analysis as your career. In that case, you need to understand the basics and work on topics like mathematical reasoning, critical thinking, business monitoring, programming, and reasoning. The best way to update skills is to work on different types of reasoning and brain teaser games, solve complex programming problems and be up-to-date with the growing trends in eh business and programming world.