What is Data Analyst
Data analytics is nothing but Collecting, Processing, Transforming, and organising the Data, making predictions, drawing conclusions, and making Decisions. It is a Multidisciplinary Field of Science in which we can use a wide range of analysis techniques and tools. In Data Analytics, we can use Maths, Statistics, and Computer Science and find insights into the data.
Data Analytics is not a small thing; it is a vast area of everything from collecting data to processing the dataset and finding out some knowledge and insight, and storing it. Nowadays, data is generated in a vast and massive amount. In simple words, we can say that it helps people or businesses to learn from past data and what is happening now and in the future.
Data Analytics can be used in many sectors, like banking, shopping, government, farming, and many more. It helps in making decisions, solving complex problems, identifying opportunities, and Improving Accuracy.
Types of Data Analytics
Four different types of data analytics can convert raw data into valuable insights.
- Descriptive analytics: it tells us what happened. In this, it helps to summarise the past data. It uses tables, charts, and averages. It can be used to compare the results.
- Diagnostic analytics: it tells us why something happened. It focuses on why something happened in the past. It can use tools like Regression, correlation, or Comparison to find out the problem. The company used to find out the reason for the drop in sales.
- Predictive analytics: it tells us what will likely happen in the future. It guesses what might happen in the future. It can use the current and past data and then find the pattern for future prediction. It can be used to find out the customer behavior.
- Prescriptive analytics: it tells us how to act. It helps to find out the best action and solutions. It gives us the choice or suggests what should be done next. Companies should use it for managing and pricing decisions.
Process of Data Analytics
- Data Collection: Data collection is the first step in which we can gather the data from different sources like websites, apps, surveys, and many other places.
- Data Cleaning: After collecting the data, data should be cleaned.Clean. So, for this, we can use the handling of missing values and remove the duplicate value. When we clean the data, it gives us an Accurate result.
- Data Analysis and Data Interpretation: After cleaning the data, we can use the mini tools like.Excel, SQL, R Language, Python. It analysed. The raw data and find the trained pattern and relationship in the data.An extract of the useful insights.
- Data Visualisation: Data visualisation is nothing but a better representation of the insights using charts, graphs, and other visualisation tools. It compares the data sets and analyzes the useful data from the raw data.
Skills Required For Data Analytics
1. Technical Skills
- Programming: Python, R (basic level for analysis)
- Data Handling: Pandas, NumPy (in Python)
- Visualisation Tools: Tableau, Power BI, Excel, Matplotlib, Seaborn
- Databases: SQL (MySQL, PostgreSQL), NoSQL (MongoDB – sometimes)
- Excel: PivotTables, charts, formulas
- Reporting Tools: Google Data Studio, Looker, Excel, Crystal Reports
2. Statistical Knowledge
- Mean, median, mode, variance, standard deviation
- Hypothesis testing, regression, correlation
3. Soft Skills
- Communication: Explain data insights to non-technical stakeholders
- Problem-Solving: Analyse business problems using data
- Attention to Detail: Avoid misinterpretation of data
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Common Job Titles for Data Analysts
- Data Analyst
- Junior Data Analyst
- Business Analyst
- Marketing Analyst
- Operations Analyst
- Financial Data Analyst
- Reporting Analyst
Career Path
Entry Level roles
Entry level we have job role like Data Analyst and Reporting Analyst
Mid level roles
In mid level we have Business Analyst and Senior Data Analyst
Advance level Roles
In Advance level we have Data Scientist and Analytics Manager
Average Salary of Data Analyst in India
In India, I have always said that the updated analysts are up to 7 LPA to 15 LPA. It can vary by different locations, different companies, and job roles. Some companies can provide the. Starting salary is up to 4 LPA.
Data Analyst vs Data Scientist
Data science is focused on analysing the path that uses tools like SQL, Python, and the R language. The role of a data analyst is to find Insights and make a report. Statistical level of data, and this is basic to intermediate. Data science focuses on predicting future outcomes using tools like Python and big data to sell me libraries. The role of a data Scientist is to use the model and make predictions. The statistical labour is advanced.
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