Living in the 21st century, you might have often come across the word ‘data analytics’. In this technical analytical world, it is considered as one of the most important tools as well as terminologies. For those who want to start their journey in data analytics, then you are at the right place and this is the right read for you. This guide explores the essentials of analytics and its types as well as career pathways. You’ll learn how to get started in data analytics by this blogpost.
Introduction to Data Analytics
It relates to the process of evaluating the datasets to draw the conclusions regarding the information they consist of. By leveraging different techniques, tools as well as algorithms, this converts the raw information or data into the valuable insights which is very helpful for the businesses.
The process of data analytics consists of collecting, cleaning, processing as well as analyzing the data to recognize the patterns and trends. From enhancing the customer experience to improving the operational efficiency that offers different forms of opportunities for growth.
Introduction to Big Data Analytics
It consists of analyzing the complex datasets and it is commonly referred to as Big Data. These datasets are characterized by the 3 V’s including the Volume, Variety and Velocity.
Generally, the Big Data uses the advanced tools to handle & analyze the data at a large scale, uncovering the insights that would be impossible while finding the smaller datasets. And one of the best things about Big Analytics is its capability for providing real-time analysis, enabling the businesses to make quick decisions.
Difference between Data Science & Data Analytics
In fact, both the fields are related and share the common techniques, there are some fundamental differences including:
| Aspects | Data Analytics | Data Science |
| Definition | It focuses on analysing of the past data | It involves predictive modelling & machine learning |
| Goal | Interpreting of the historical data | Generating the models& algorithms |
| Focus | It focuses on historical data | It focuses on future predictions |
| Skillset | Requires the proficiency in Statistics, visualization tools and SQL | Requires proficiency in programming, machine learning & fe algorithms |
| Scope | It focuses on Data Analysis | It focuses on data extraction, processing, etc. |
| Output | Reports, Dashboards & descriptive statistics | Predictive models, algorithms & recommendations |
| Tools Used | Tableau, Power BI, Google Analytics | Python, R, Hadoop |
Types of Data Analytics
There are four main types, including:
- Descriptive Analytics
It generally focuses on interpreting the historical data to understand the past behaviour as well as events, it answers the questions through the reports, data visualizations as well as dashboards
- Diagnostic Analytics
This analytics digs deeper into the data to understand why something happened. It analysis historical data & while identifying of the patterns, this analytics provides the answers to the questions such as “why did this happen”
- Predictive Analytics
This analytics uses statistical models as well as machine learning tools and techniques to forecast the future trends based on the historical data. It helps the businesses to anticipate the events & make informed decisions
- Prescriptive Analytics
This analytics goes a step forward, by offering the recommendations on actions to take based on the predictive insights
Also read: Data analytics interview questions and answers
Data Analytics Jobs
It offers a variety of career opportunities and paths or professionals with the different skillsets, here are some of the common jobs:
- Data Analyst
This job role mainly focuses on collecting, processing and analyzing of the data to produce the actionable insights, The Data Analyst are skilled in the tools including Excel, SQL & Data visualization platforms including Tableau & Power BI
- Data Scientist
These professionals use the advanced programming & machine learning tools and techniques to predict the future trends and to develop the algorithms
- Business Intelligence Analyst
These professionals mainly focus on transforming data into the business insights and helps the businesses to improve their decision making processes
- Data Engineer
The infrastructure that enables data scientists and analysts to deal with massive datasets is created and maintained by data engineers.
- Machine Learning Engineer
The development and implementation of machine learning models that are capable of analyzing and forecasting data patterns is the area of expertise for machine learning engineers.
Data Analytics Projects
Some project ideas include:
- Customer Segmentation
- Sales Forecasting
- Sentiment Analysis
- Fraud Detection
Roadmap for Data Analyst
- Learn the Basics
Learn the basics of statistics, data visualization, and data analysis first.
- Master SQL
An essential tool for accessing and examining data from relational databases is SQL.
- Learn Data Visualization
You can make insightful charts, graphs, and dashboards with the assistance of data visualization tools like Tableau and Power BI.
- Understand Statistical Methods
To apply these techniques to actual data, learn fundamental statistical concepts including regression, probability distributions, hypothesis testing, and more.
- Explore Advanced Tools
Learn computer languages like Python and R, as well as big data platforms and machine learning packages, for advanced analytics.
- Work on Projects
As previously said, use your abilities in practical tasks to show that you can manage real-world data difficulties.
Explore: Learn data analytics with 6 in-demand tools
Conclusion
Starting a career in data analytics is an interesting and valuable option. The best time to begin is now more important than ever because of the increasing and growing requirement for skilled employees in this industry. Taking a course will provide you the information and practical experience you need to succeed, regardless of whether you want to start from scratch or advance your career.
FAQ’s
It refers to the process of evaluating the complex datasets to uncover the patterns, trends and insights which enables the businesses to make a better and informed decisions.
You can either learn through online or by joining to the course, it’s important to gain practical knowledge and experience with the tools including SQL, python and tableau.
