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Data Analytics Tutorial for Beginners

Data Analytics Tutorial Overview

In general, when data is processed quantitatively and qualitatively to generate some useful insights from it, the process is known as Data Analytics. There are many steps included from extracting data to categorizing it to finding some patterns, connections, and other information that may be an asset to the organization.

The topics which we are going to cover today are:

Watch this Data Analytics for Beginners tutorial:

Data Analytics Tutorial for Beginners Data Analytics Tutorial for Beginners

What is Data Analytics?

The science of studying raw data to draw conclusions about it is known as data analytics. Data analytics can assist a business in optimizing its performance, performing more efficiently, maximizing profit, and making more strategic decisions.

For the most extensive data manipulation, data analytics relies on several software tools, including spreadsheets, data visualization, and reporting tools, data mining applications, or open-source languages.

Importance of Data Analytics

Data analytics is a crucial component of giving firms a competitive edge. Here are a few ways that will explain why data analytics are critical for businesses today:

Certification in Bigdata Analytics

Difference between Data Science and Data Analytics

Although the terms are frequently used together, data science and data analytics are separate fields, with the range of scope being the primary difference. Data science is an umbrella phrase for a variety of fields that are used to mine massive datasets. On the other hand, Data analytics provides a much more specialized form of this, which can even be regarded as a component of a larger procedure. Analytics is dedicated to generating effective results that may be implemented right away based on existing queries.

Exploration is another major difference between the two. The goal of data science is to seek out information by filtering through enormous databases, sometimes in an unstructured manner. It is not concerned with providing answers to specific questions. Data analysis works best when it is focused, on questions that need to be answered using existing data. Data analytics prioritizes finding answers to questions that have already been posed, whereas data science generates larger insights that focus on questions that should be answered.

Data Scientists must be fluent in mathematics and statistics, as well as have experience in programming (Python, R, SQL), predictive modeling, and machine learning. Data analysts must be proficient in data mining, data modeling, data analysis, and database management and visualization. Data Scientists and analysts must be strong problem solvers & critical thinkers.

Data Analytics tools

Now let’s discuss some tools which are widely used in Data Analytics:

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Types of Data Analytics

Data Analytics can be of 4 major types:

Data Analytics Lifecycle

The Data Analytics Lifecycle is a cyclical process that outlines the creating, gathering, processing, implementing, and analyzing of data for various purposes in six different stages:

  1. Discovery – During the first phase of data analytics, stakeholders regularly execute the following tasks: assess business trends, conduct case studies of similar data analytics, and research the domain of the business industry. The entire team evaluates the internal infrastructure, internal resources, the total time required, and technical requirements. As soon as all of these analyses and evaluations are finished, the stakeholders begin developing the basic theory for solving all business difficulties in the context of the current market situation.
  2. Data preparation – After the data discovery phase, the data is prepared in the second step by changing it from existing systems into a data analytics form through the sandbox platform. A sandbox is a scalable platform often used by data scientists for data preprocessing. High-capacity storage, large CPUs, and large I/O capacity are all included in it. The stakeholders involved in this phase are largely involved in data preprocessing for preliminary results using a typical sandbox platform.
  3. Model planning – The third part of the life cycle involves model planning, in which the data analysts properly plan the methodologies to be adapted and the numerous workflows to be followed throughout the next stage of model construction. At this point, the team decides on the various divisions of work in order to precisely specify the workload amongst team members. The previously prepared data is further investigated in order to fully understand the numerous features and their correlations as well as to conduct feature selection for the model.
  4. Model building – The next stage of the cycle is model creation, in which the team creates datasets for training, testing, and production. Additionally, the model is put into action depending on the planning done in the earlier phase. The type of environment necessary for the model’s execution is chosen and set up in advance, allowing for the application to a more durable environment as needed.
  5. Communicate results – Phase five of the life cycle examines the project’s outcomes to determine whether it was successful or unsuccessful. The outcome is carefully examined by the whole team and its stakeholders in order to make conclusions about the important discoveries and compile all of the work done. The business values are also quantified, and a detailed narrative of the main findings is developed and shared with the various stakeholders.
  6. Operationalization – The team prepares a final report, together with briefings, source codes, and relevant papers, during phase six. The final phase also includes executing the pilot project to implement and evaluate the model in a real-time context. Data analytics delivers value to people, customers, business sectors, and other organizations by enabling the development of models that improve decision-making.

How to Become a Data Analyst?

You’ll need academic qualifications along with some specific skills to begin your career in data analysis.

Academic Qualifications

Having a decent CGPA and a graduation degree from a data analysis program is recommended. Even if a person does not specialize in data analysis, a degree in mathematics, statistics, or economics from a well-known university can lead to an entry-level Data Analyst position.

The majority of entry-level data analyst positions require at least a bachelor’s degree. Higher-level data analyst jobs often pay more and may require a master’s degree. Aside from the degree, a person interested in becoming a Data Analyst may enroll in online courses.


You can start preparing right away by going through these Data Analyst Interview Questions now!

Career Scope

A data analyst may expect great compensation, engaging work, and outstanding job security. This is a career that is always changing and requires great attention to detail and a focus on quality.

Data Analyst is a position that is clearly on the rise. The difference between mid-level and senior-level positions is determined by experience and extra education. However, due to the high demand for Data Analysts at all levels, the expected job growth for each tier over the next decade ranges from 5% for Financial Analysts to 25% for Operations Research Analysts.


One of the key elements pushing some of today’s biggest and greatest firms ahead is data analytics. In this highly competitive environment, businesses that can turn data into useful information will undoubtedly succeed. Hence, Any business that uses data analytics effectively can easily outperform its competitors.

You can enroll yourself in Data Science Training to begin your Data Science and Data Analytics career!

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