If you’re looking to become a data analytics professional, there are a few things you’ll need to know.

The first thing is that data analytics is a broad field that encompasses many different types of jobs. You can work as an analyst, for instance—meaning that you’ll be analyzing data to help make decisions about business strategy or marketing campaigns. Or you could work as a programmer who uses complex algorithms to create software programs that help companies make decisions about everything from product development to supply chain management.

There are also specific job titles within the industry like “data scientist.” This job requires advanced statistical knowledge and involves using machine learning techniques to identify patterns in large datasets and make predictions about them.

To become a data analytics professional, it’s important that you have strong analytical skills and excellent communication skills. It’s also important that you have strong quantitative skills like math and statistics so that you can use them when performing your analysis tasks at work.

How To Become Data Analytics

Here are five steps to consider if you’re interested in pursuing a career in data science:

1. Earn a bachelor’s degree in a field with an emphasis on statistical and analytical skills, such as math or computer science
2. Learn important data analytics skills
3. Consider certification
4. Get your first entry-level data analyst job
5. Earn a master’s degree in data analytics

What Does a Data Analyst Do?

A data analyst collects, processes and performs statistical analyses on large dataset. They discover how data can be used to answer questions and solve problems. With the development of computers and an ever increasing move toward technological intertwinement, data analysis has evolved. The development of the relational database gave a new breath to data analysts, which allowed analysts to use SQL (pronounced “sequel” or “s-q-l”) to retrieve data from databases.

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Case Western Reserve University info

CWRU Data Analytics Boot Camp

CWRU Data Analytics Boot Camp is a rigorous, part-time program that prepares students with the fundamental skills for data analytics and visualization. Through hands-on, in-person instruction, you’ll cover a wide range of topics and graduate ready to apply your skills in the workforce.

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Columbia University info

Columbia Engineering Data Analytics Boot Camp

Are you ready to become a data-driven professional? Columbia Engineering Data Analytics Boot Camp is a challenging, part-time bootcamp that equips learners with the specialized skills for data analytics and visualization through hands-on, in-person classes.

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University of California, Berkeley info

Berkeley Data Analytics Boot Camp

Turn data into actionable insights. Berkeley Data Analytics Boot Camp is a dynamic, part-time program that covers the in-demand tools and technologies for data analytics and visualization through rigorous, project-based classes.

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Georgia Institute of Technology info

Georgia Tech Data Science and Analytics Boot Camp

Expand your skill set and grow as a data scientist. Georgia Tech Data Science and Analytics Boot Camp covers the skills needed to analyze and solve complex data analytics and visualization problems.

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University of Texas at Austin info

The Data Analysis & Visualization Boot Camp at Texas McCombs

The Data Analysis and Visualization Boot Camp at Texas McCombs puts the student experience first, teaching the knowledge and skills to conduct data analysis on a wide array of real-world problems. Students dive into a comprehensive curriculum, learning how to collect, analyze, and visualize big data.

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University of Southern California info

USC Viterbi Data Analytics Boot Camp Affiliated with Trilogy Education Services

Expand your skill set and grow as a data analyst. This program covers the specialized skills to be successful in the field of data in 24 weeks.

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Typical Data Analyst Job Description

Most jobs in data analytics involve gathering and cleaning data to uncover trends and business insights. The day-to-day data analyst job varies depending on the industry or company or the type of data analytics you consider your specialty. Data analysts may be responsible for creating dashboards, designing and maintaining relationship databases and systems for different departments throughout their organization using business intelligence software, Tableau, and programming.

Most data analysts work with IT teams, management and/or data scientists to determine organizational goals. They mine and clean data from primary and secondary sources then analyze and interpret results using standard statistical tools and techniques. In most cases, they pinpoint trends, correlations and patterns in complex data sets and identify new opportunities for process improvement. Data analysts must also create reports on their findings and communicate next steps to key stakeholders.

Data Analyst Qualifications

Skills Required for Data Analysts

• Programming Languages (R/SAS): data analysts should be proficient in one language and have working knowledge of a few more. Data analysts use programming languages such as R and SAS for data gathering, data cleaning, statistical analysis, and data visualization.
• Creative and Analytical Thinking: Curiosity and creativity are key attributes of a good data analyst. It’s important to have a strong grounding in statistical methods, but even more critical to think through problems with a creative and analytical lens. This will help the analyst to generate interesting research questions that will enhance a company’s understanding of the matter at hand.
• Strong and Effective Communication: Data analysts must clearly convey their findings — whether it’s to an audience of readers or a small team of executives making business decisions. Strong communication is the key to success.
• Data Visualization: Effective data visualization takes trial and error. 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 PostreSQL, 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 modeling and analytics techniques.
• Machine Learning: Data analysts with machine learning skills are incredibly valuable, although machine learning is not expected skill of typical data analyst jobs.

Data Analyst Responsibilities

A Day in the Life of a Data Analyst

The day-to-day for a data analyst depends on where they work and what tools they work with. Some data analysts don’t use programming languages and prefer statistical software and Excel. Depending on the problems they are trying to solve, some analysts perform regression analysis or create data visualizations. Experienced data analysts are sometimes considered “junior data scientists” or “data scientists in training.” In some cases, a data analyst/scientist could be writing queries or addressing standard requests in the morning and building custom solutions or experimenting with relational databases, Hadoop and NoSQL in the afternoon.

“A big part of my job is creating player projections for Fantasy Baseball. These power the default rankings in our draft rooms and inform my preseason and in-season rankings of players. Our readers and customers of our Fantasy product rely on the accuracy of these projections, so it’s important to have a sound statistical basis for making them.During the season, we have a high degree of interaction with our audience, as a large part of our responsibility is to respond to questions about player value and performance. Statistical analysis informs these recommendations, whether they are made through social media platforms, written and video content, or podcasts”

–Al Melchior, a Fantasy Sports Data Analyst for CBSSports.comExternal link:open_in_new

What tools do data analysts use?

Here are some other important tools data analysts use on the job:

• Google Analytics (GA): GA helps analysts gain an understanding of customer data, including trends and areas of customer experience that need improvement on landing pages or calls to action (CTAs)
• Tableau: Analysts use Tableau to aggregate and analyze data. They can create and share dashboards with different team members and create visualizations
• Jupyter Notebook system: Jupyter notebooks make it simple for data analysts to test code. Non-technical folks prefer the simple design of jupyter notebooks because of its markdown feature
• Github: Github is a platform for sharing and building technical projects. A must for data analysts who use object-oriented programming
• AWS S3: AWS S3 is a cloud storage system. Data analysts can use it to store and retrieve large datasets