Data analysts play an important role in today’s world where data is everywhere. They help us in businesses make smarter and quick decisions. This improves them in business operations and keep us stay ahead of their competitors. As we can know every company relies on data these days, so the demand for skilled analysts keeps growing.
So, there is question arise that what does it take to be a successful data analyst?
While it’s hard to tell each and everything an analyst needs to know, but here are the key and essentials skills, tools, and areas of knowledge that are most important for the data analyst and must know.
1. Data Collection and Sourcing
The first step in any data analysis is the having and understanding the right data to acquire more knowledgeable information. Data analysts need to be good at finding reliable sources of information so that we can easily make analysis. Some common ways to collect data include:
SQL
It has two types of database RDBMS and DBMS. RDBMS or relational database management system is for tabular form data where we can select data from multiple tables. DBMS or database management system is also a tabular data but there is not relationship between tables so we can’t select data from multiple tables here. SQL is developed to manage and stored data in large volume and we can handle data, manipulate data, modify data very easily without any issue.
Web Scraping
It is also known as web harvesting or web data extraction. This is a process to get data from websites. If data is not available in a readily form, analysts may use web scraping to gather data from websites.
Flat Files
Data analysts must be good to work with files like CSV, JSON, or Excel. Many times an analyst can get these files to work on. These files work as a database for many apps and websites.
2. Data Cleaning and Transformation
Once the data is collected, the next big task is data cleaning. Raw data often may have errors, duplicates, or missing information so, we need to do it to analysis the data and understand what we want from it. It is essential to ensure that our data is clean, trimmed, and well-organized to ensure the quality of data. If the quality of data is poor then it can lead to inaccuracy where we will be unable to predict the results.
Handling Missing Data
Your data can have a few empty values. Here you decide how to deal with missing values. Either you will remove such records or repeat a top or bottom value.
Data Transformation
Most of time we don’t get data as we want. We can be changing the data into the right format or converting text to lowercase or normalizing numbers, etc.
Merging Data
Data is divided into multiple tables so we use SQL joins or tools like Python to combine the data. So, combining data from different sources to get a fuller picture is merging the data.
3. Statistical Analysis
The very first step, we need to take is to analyze the data and understand what we want from it. Statistical analysis helps in fetching trends, patterns, and relationships from the quantitative data.
Let’s have a look on different types of statistical approaches:
- Descriptive statistics: It involves collecting of data, summarizing and visualizing data to understand its central tendency, convert into meaningful data, analyzing and summarizing data in the form of charts, graphs and tables at the it simply makes the complex into simple data for easy to read and better understanding.
- Hypotheses Testing: It is a process of statistical inference that is utilized to determine whether the data is sufficiently effective a particular hypothesis. It is a formal procedure for innovations of new Ideas about the world using statistics.
- Inferential statistics: It is different from descriptive statistics as it is used to make predictions and draw conclusions from the population data from sample data.
4. Data Visualization
Data visualizations are the integral part of the data analysis. We can create highly interactive visualizations out of data. We can choose from a variety of chart types, bar charts, line graphs, pie charts, heat maps, and geographical maps to visualize any dataset. These visualizations help us to identify trends, outliers, and patterns from the data very quickly.
A few popular tools to create data visuals:
Excel
It provides a wide variety of charts and graphs that can help make to analysis data and get more insightful and well presentable in the reports.
Power BI
It is the best features that is provide to us its visuals it provides numerous of visuals where we can easily make data in the suitable visual forms.
Tableau
This tool is a more advanced tool for creating interactive dashboards and visualizations as compare to other tools.
Python & R Languages
These languages are for those who prefer coding. Python libraries like Matplotlib, Pandas, and Seaborn help create charts and graphs. Similarly, R also offers various packages to create data visualization.
5. Programming Skills
Good programming skills are expected from all the data analysts as it helps in saving time while working on large datasets. Knowledge of programming languages helps in automatic various tasks also.
The most common languages like Python, R, and SQL.
6. Business Understanding
Understanding of business is very essential. It confirms that analyst knows what to do with the given data. If an analyst knows how a business works and what are the measures matter most, he will be making findings more relevant. Key things to know include:
Identifying Key Measures
Knowing which business criteria (like sales growth) matter the most.
Framing the Right Questions
Understanding how to define problems so that data can help answer them or provide them insights they want.
Communicating with Stakeholders
It is the last and very important duty of an analyst. The people you are presenting your data may not have a technical background, so explain complex data in a simple way that makes sense.
7. Data Reporting and Communication
Analyzing data is just one part of this job but we need to make reports and summarize the data also. These reports are for the stakeholders, CEO, customers, and the various people waiting for. Some key skills in this area include:
Storytelling with Data
I would say, it is an art. Being able to present the data in a way that tells a clear story is very useful. Every stakeholder or your senior expect it from you. That’s why Tableau has feature called story.
Writing Reports
Making reports is a second specialization after data analysis. Analyzing data is a first thing but preparing simple, short, and clear reports that summarize findings and suggest actions is another important skill.
Presentation Skills
It is where you present your final product. Presenting data to others in a way that’s easy to understand, using visuals and key points.
Final Words from the Expert
As a data analyst, a few important skills are required like technical skills, business understanding, and communication abilities. Whether it’s collecting the data, cleaning it, analyzing it, presenting findings, or visualizing the data – analysts need to be versatile. As the technology keep making itself advance day by day so we need to keep learning and adapting to new tools and methods. Learning data analysis and Machine Learning will continue to play a key role in helping businesses make better decisions.
