Data analytics involves analyzing raw data to derive actionable insights that are meaningful in solving business decisions, which also helps businesses use past data to predict future trends and behaviors and have a better understanding of their audience and the market as a whole. As the world becomes more technologically advanced, it has become one of the most in-demand careers in technology. But it is important to have the right background and skills to succeed in this role.
In order to start a career in data analytics, you would need to know the following:
1. Required skills: You do not necessarily have to have a background in computer science, statistics, finance, economics, or information management to start up a career or become a data analyst; all you need is the right skill, training, and certification. However, some of the required skills include the numeric skill, which involves using probability and statistics to conduct analysis; communication skills, which involve being able to present complex ideas and explain results from the analysis; and analytical and problem-solving skills, which involve being able to suggest smarter business decisions based on conclusions from the analysis carried out.
2. Learn Data Analytics Fundamentals: Data analytics tools such as advanced Microsoft Excel, programming languages such as SQL, Python, and R, Spark and Hadoop for number-crunching, and Tableau, Matplotlib, or ggplot2 for creating beautiful visualizations that bring data to life are all very important and can be a great way to learn the fundamentals of data analysis. These tools would likewise give you the ability to gain hands-on experience with data management, data cleaning, data visualization, statistical programming, and programming languages.
3. Developing your data analytics skills: This can be accomplished by working on projects or applying for internships. Alongside doing this, it is also important to keep up-to-date with the latest and greatest tools associated with data analysis, which include spreadsheets and querying languages, programming languages and frameworks commonly used by data analysts like Python, and visualization tools such as Tableau and Power BI. However, it is much more advisable to put together practice projects that touch on all the different stages of data analysis, which include researching companies and market opportunities, establishing the parameters of the data you need to collect, gathering and cleaning that data, then modeling and analyzing it using custom-built algorithms.
4. Develop visualizations: finding the best way to let the data speak for itself is top-notch; however, to do this, you would need to practice building your own visualizations from scratch as this can be used to present your findings.
5. Develop a Data Analyst Portfolio: In showcasing your work, you would be required to develop a portfolio where you can demonstrate your data skills and knowledge. You could also join data analyst communities to expand your knowledge, find inspiration, and research, develop, and complete a personal project while carefully documenting and visualizing its creation.
6. Apply for relevant data analyst jobs: With data skills, you can get on-site and remote data analyst jobs, some of which can be entry-level or more senior data roles.
Most importantly, as you are advancing and moving through the data analytics career, you should also consider advancing and obtaining certifications such as Certified Analytics Professional or Cloudera Certified A.