The field of data science has rapidly become one of the most sought-after for professionals from a variety of backgrounds. Specialist data analysts lie close to the top of the food chain, with healthy salaries and benefits.
If you are wondering about data analysts, what they do, what are the skills and qualifications you require and so on, the article will give you clarity for your track. But, before moving forward about who are data analysts, you first need to know what is data analytics.
Data Analytics
Data Analytics refers to operations assisting in analysing raw data to draw some meaningful conclusions regarding that information. It helps analysts, investigators, scientists, as well as in business to optimize performance.
It is coined with two words – ‘Data’ and ‘Analytics’. ‘Data’ is some meaningful information built around observations. ‘Analytics’ refers to the study based on historical data to potentially analyse trends or the performance of tools or the effort of certain decisions.
Data Analysts
A Data Analyst collects data and analyses it to predict or examine the trends or the effect of certain decisions. They analyse all the numeric and other kinds of data and translate it into understandable language, this data is then used by upper management to make important business decisions.
They are the actual troopers of Data Science. Data Analyst is the one who is concerned with gathering certain data, structuring databases, generating and managing models, and composing advanced types of analysis to explain the pattern in the data that have already emerged.
Data analyst VS Data scientist
These two terms often confuse one, by understanding the questions below we may conclude how they are different
Data Scientist?
A professional who analyses both structured and unstructured data, strictly from a business point of view. He is responsible for delivering the prediction that aids in business value. They identify the right areas from where they can find relevant patterns to help in case any business-related problem arises.
The similarity between the Data Scientist and Data Analyst?
The task of a data analyst can be performed by a data scientist as well such as collecting data, analyse trends to predict certain decisions.
Difference between Data Scientist and Data Analyst?
The main difference between the two is how they use the data.
| DATA SCIENTIST | DATA ANALYST |
| A data scientist is the one who examines the trends based on previous patterns | A data analyst is the one who predicts some meaningful vision from data |
| Data scientist directs the business complications | The data scientist also picks up those issues that will have the most business value once solved |
| Data scientist job role involves estimating the unknown | Data analyst job role involves looking at the known from new perspectives |
Responsibilities of a Data Analyst
1. Use statistical techniques to interpret data and analyze results.
2.Collecting both structured and unstructured data.
3. Identify, evaluate, and explain trends in the data set to examine particular decisions.
4. Generating reports from single or multiple systems.
5. Develop and implement databases, data collection systems, data analytics and other strategies that optimize statistical efficiency and quality.
6. Designing and maintaining data systems and databases.
7. Work with management teams to initiate business needs.
Skills Required
- Programming Languages (R/SAS): Data analysts use programming languages such as R and SAS for data gathering, data cleaning, statistical analysis, and data visualization.
- Creative and Analytical Thinking: This helps the analyst to generate interesting research questions that will enhance a company’s understanding of the matter at hand.
- Strong and Effective Communication: Strong communication is the key to success.
- Data Visualization: A successful data analyst understands what types of graphs to use, how to scale visualizations, and know which charts to use depending on their audience.
- Data Warehousing: Some data analysts work on the back-end. They connect databases from multiple sources to create a data warehouse and use querying languages to find and manage data.
- SQL Databases: SQL databases are relational databases with structured data. Data is stored in tables, and a data analyst pulls information from different tables to perform analysis.
- Database Querying Languages: The most common querying language data analysts use is SQL, and many variations of this language exist, including PostgreSQL, T-SQL, PL/SQL (Procedural Language/SQL).
- Data Mining, Cleaning and Munging: When data isn’t neatly stored in a database, data analysts must use other tools to gather unstructured data. Once they have enough data, they clean and process through programming.
- Advanced Microsoft Excel: Data analysts should have a good handle on excel and understand advanced modelling and analytics techniques.
- Machine Learning: Data analysts with machine learning skills are incredibly valuable, although machine learning is not the expected skill of typical data analyst jobs.
Data Analyst: Education and Qualification
Data Analyst Education:–
There are some positions which may only require a high school diploma, data analyst education prerequisite usually consist of at least a bachelor’s degree. Data analyst classes may cover topics in:
1. Data analytics
2. Data mining
3. Scripting
4. Database design
5. Statistics
6. Project management
Data Analyst Qualification:–
Most employers expect candidates to have a specific computer and analysis skills. Although specific qualifications vary by employer, some of these necessary skills may include, but are not limited to:
1. Microsoft Office and Excel
2. Structured Query Language (SQL)
3. R or Python (statistical programming)
4. Data visualization
5. Data manipulation
Many employers also want candidates to have the ability to work in teams and communicate well verbally and in written form. Applicants should also be well-organized and pay close attention to detail. Some jobs may also require analysts to work with confidential information, so discretion is needed.
Companies hiring Data Analyst
- Mu Sigma Analytics
- Accenture Analytics
- Fractal Analytics
- Manthan
- Absolut Data
- Cartesian Consulting
- Latent View
- Unmetric
- Convergytics
- SIBIA Analytics
Books to refer
- Data Analytics Made Accessible – by A.Maheshwari
- Predictive Analytics: The power to predict who will click, buy, lie, or die – by E.Siegel
- Too Big to Ignore – by P.simon
- Learn Analytics – by A.Croll and B.Yoskovitz
- Data Smart – by J.W. Foreman
- Developing Analytic Talent – by V.Granville
– Jaya Gupta
Utkarshini Journal 