Let's talk about Data Analytics

Role of Data in everyday life

Data plays a vital role in our everyday life. Directly or indirectly, for daily life decisions, we depend on some data, be it choosing a novel to read from a list of books, buying a thing after considering the budget, and so on. Have you ever imagined searching for something on Google or Yahoo generates a lot of data? This data is essential to analyze user experiences. Getting recommendations on various e-commerce websites after buying a product and tracking parcels during delivery are part of Data Analytics which involves analyzing the raw data to make informed decisions. But this raw data does not help make decisions if it has some redundancy, inconsistency, or inaccuracy. Therefore, this data needs to be cleaned before considering for analysis.

Introduction to Data Analytics

Data Analysis is a kind of treasure hunt. In this game, we have to connect different clues and solve the problem. Similarly, in Data Analytics, we are provided with specific business problems, and we need to find their solutions after interpreting the data very carefully by connecting different data points. So, if you love the treasure hunt and want to solve a problem, then data analytics might be the right field for you.

Data Analytics is a field that involves exploring and analyzing data to discover patterns, and unknown trends, finding correlations and acquiring insights from the datasets. This helps a business improve decision-making, effective marketing, and provide better customer services. The first step of a data analyst is to understand the business problem properly after considering the business goals. Then, the required data must be gathered from many relevant sources. This step is followed by data cleaning, where the data analyst needs to clean up the messy, raw data by eliminating redundancy and fixing the missing values. Then there is a need to perform Exploratory Data Analysis (EDA), where data is visualized using business intelligence tools such as Power BI and Tableau. These visualizations help to predict future outcomes and also help to provide data-driven solutions for business problems.

Types of Data Analytics

The broad types of data analytics include:

1. Descriptive Analytics describes what has happened in the dataset. It focuses on summarizing the dataset’s essential aspects.
2. Diagnostic Analytics focuses on why the problem has happened. It deals with the reasons that are responsible for the business problem.
3. Predictive Analytics focuses on what could happen. It describes the likelihood of the events that might occur.
4. Prescriptive Analysis uses previous results and patterns to identify what must be done to meet future objectives.

Skills required for Data Analysis

One needs to have the following skills to become a Data Analyst:

1. SQL (Structured Query Language): This is used to access and manipulate databases.
2. Python or R: Such programming languages help to analyze and visualize data.
3. Microsoft Excel: Data analysts can benefit from various Excel tools, such as functions, pivot tables, and visualizations.
4. Basic Mathematics: Data Analysts understand fundamental mathematics, focusing majorly on Linear Algebra, Probability, Statistics, and Calculus topics.
5. Business Intelligence (BI) tools such as Tableau or Power BI: These are used to make interactive dashboards to visualize the data efficiently. 

Besides these, a Data Analyst must possess effective communication and critical thinking skills. A problem-solving and analytical mindset help to think about the business issue deeply and come up with the best solutions.

Platforms for learning about Data Analysis

One can use various platforms, such as Coursera, YouTube, and Google, to learn different skills and tools required for data analysis. After learning these skills, a project portfolio can be built, and the dataset can be taken from sources such as Kaggle and data.gov. One can also join internships related to Data Analysis and Visualization. These will help to begin a career in Data Analytics.

Image reference: https://www.edx.org/course/data-analytics-for-everyone