Data Analyst is one of the hottest jobs of the 21st century. Many students after graduating are willing to work as an entry-level data analyst in big multi-national companies and corporate organizations.
In this fast-paced world, choosing data analytics as a career is a smart choice. Moreover, the demand for entry-level data analysts is growing day by day across the globe. Organizations are also willing to pay hefty paychecks to well-deserving professionals.
After spending a considerable amount of time on getting an inside look about what it takes to become a data analyst and what you can expect at entry-level, I’ve decided to write this ‘Ultimate Guide’ for students who want to start their career in the field of data analytics or as an entry-level data analyst.
Let’s get right into it!
INTRODUCTION TO DATA ANALYTICS AND EXPECTATIONS AT ENTRY-LEVEL
Data Analytics is the process of deriving information and useful insights through raw data sets within the databases. The process involves data validation, cleaning, analyzing, visualization, etc. to name a few from which useful insights and information are derived. This information helps in making better decisions and strategies within an organization. In today’s business world, it is necessary to ensure the quality of data before analyzing better understanding or accurate information, and insights. This is considered one of the top skills and courses to enroll in the 21st century as mentioned in my recent post. A dataset undergoes evaluation based on various data quality dimensions in the initial phase. The data should be of correct values. The data items with value meta-data should have relevance and uniqueness as duplicate records are deleted etc. to name a few.
The process of data analysis involves various stages including data requirements, data collection, data processing, and data cleaning. Thereafter, different techniques called as exploratory data analysis is applied to interpret the information from the datasets. Different machine learning algorithms and mathematical formulas are applied to establish relationships such as correlation etc. from variables within the datasets. Thereafter, you need to analyze the pattern and trends from the datasets.
Once the data is analyzed, data visualization is done. In simple words, data visualization is the representation of data insights and information through graphical representation. It may include graphs, pie-charts, heat maps, dashboards, etc. Since the human brains process visual information more effectively than written reports or documents, data visualization helps to interact with data more effectively and helps to communicate findings more constructively.
REQUIREMENTS FOR ENTRY LEVEL DATA ANALYST
Entry-level data analysts require some educational qualifications and subjects to study to begin a career in the field of data analytics.
- Degrees such as Bachelor’s or Master’s in the study fields including computer science, information technology or any ICT related from an accredited college or university
- Prior experience through various internships or project work inclined towards Data Analytics
- Ability to analyze and work on large databases and perform complex queries to derive information
- Proficient in various Softwares and tools such as Tableau, Power-BI, MS-Excel, for data visualization, pivot tables, lookup, etc.
- Good Programming Language skills, specifically in Python, R, SQL, etc. is a must and strong blend of logical and analytical skills
- Excellent written and speaking skills and ability to work in a team and work under extreme pressure or deadlines
ESSENTIAL SKILLS REQUIRED FOR ENTRY LEVEL DATA ANALYST
You should have good technical knowledge to excel in the field of data analytics. Many organizations look for entry-level data analysts to have a good understanding of different programming languages, software, and tools, etc. Some of the essential skills that you should do and have a solid understanding are:
Structured Query Language or SQL is the standard language that is used for RDBMS or relational database management systems. SQL helps in updating or retrieval of data from databases within a database management system. Microsoft SQL Server, Oracle, etc. are some of the relational database management systems that work on Structured Query Language. You should have a strong foundation in this query language because it will be essential in the various processes involving data analysis.
Python is one of the most famous programming languages used for data analysis. Different libraries within Python come under play which helps in doing data analysis. Pandas is one of the most famous data analysis libraries in Python to import datasets from spreadsheets in Excel or to perform time series analysis. Different specialized libraries such as SymPy for analyses related to statistics or PyLearn and Shogun for machine learning etc. Also, SQLite3 or csvkit used for data formatting and storage. You should try to practice regularly python programming skills.
R is another famous programming language in performing data analytics. This programming language is commonly used by data professionals or statisticians to perform data analysis or developing statistical complex software applications. This language works in a command-line interpreter and is an interpreted language. R and its libraries provide a wide range of techniques such as clustering, statistical tests, etc. It also provides static or dynamic and interactive graphics with the use of additional libraries. R also supports matrix arithmetic and data structures include arrays, lists, data frames, etc.
Microsoft Excel is a spreadsheet software developed by Microsoft Corporation across different OS platforms such as Windows, macOS, Android, and iOS. This software helps data analysts to perform a variety of operations and applications such as calculations, graphic tools, pivot tables, and macro programming language also known as Visual Basic for Applications. This software helps in organizing large sets of datasets in a well-defined manner and efficiently doing data analysis.
Tableau is one of the most famous tools specifically for data visualization. This software can handle a tremendous amount of data and support various scripting languages. Tableau helps in the creation of interactive dashboards and organizing data insights in such a manner so that it can be easily accessed and analyzed. This is very helpful in data-driven business decisions and strategies. It provides a great user experience and is easy to use and interpret information.
6. POWER BI
Power BI is another most famous data visualization tool. It enables you to create interactive dashboards and visualizations. Power BI unifies large data sets from different sources to create immersive reports which provide important and actionable insights for better decision-making strategies to drive business results.
CERTIFICATIONS FOR ENTRY LEVEL DATA ANALYST
Data Analyst Certifications are the best way to showcase your expertise and gain a competitive edge. It will also help in expanding your knowledge and skills. These certifications will also help in building professional credibility and recognition. Also, the time and hard work you devoted to your certification will help you in increasing your earning potential. Also, according to one recent study, certifications increased the earning potential of professional certification holders by 20 to 40%.
Below are some of the certifications from reputed organizations which are valuable and look good in your CV:
- Microsoft Certified Solutions Expert (MCSE): Data Management and Analytics
- Certified Analytics Professional (CAP)
- Cloudera Certified Associate (CCA) Data Analyst
- SAS Certified Data Scientist Using SAS® 9
SEARCH FOR DATA ANALYTICS INTERNSHIP
Internships play a key role in getting placed in a well-reputed organization and help in providing corporate exposure. Also, you will be getting to work on different projects under the guidance of experienced professionals. Many internships opportunities regarding specifically for data analytics aspirants are available on various platforms including LinkedIn, Glassdoor, Indeed, etc. throughout the year. These internships will help in improving your skillset whether it is working with programming languages to analyze data or to work with different tools and software for visualization. Besides, companies these days prefer students at an entry-level who do have some prior experience in this field through various internships. In this way, you will stand out among others as you have acquired some practical experience.
Some platforms where you can find internships actively regarding data analytics are :
- Insidesherpa provides a virtual internship program in the field of data analytics in collaboration with KPMG, Australia
SALARY OF ENTRY LEVEL DATA ANALYST
An entry-level data analyst can expect a salary of $ 63,355/yr in the United States according to Glassdoor. However, different factors play an important role in determining your paycheck. Earning a degree from a reputed university typically has a positive influence on your pay scale. Various projects that you have undertaken as part of your thesis during graduation also plays an important part. Besides, successfully completing various internships revolving around data analytics can have a good impact in the eyes of the recruiter. Also, certifications from reputed organizations will help with an increase in pay scale.
Lastly, it depends upon your skills acquired during graduation. The ability to analyze and work on data, identifying new complex techniques, willingness to learn new things, and thinking out of the box are some main key areas that showcase your desire and urge to excel in this field. Let me know if you find this post helpful