Hi, I’m Olga! I have years of experience in data science, most recently at eBay. Now, I work as an industry mentor at Pathrise. Here, I help data scientists land great jobs through technical workshops and 1-on-1s. Check out my article where I answer the question, “What does a data analyst do?”
Under the data science umbrella, there are quite a few different roles, including data analyst. The line between data scientist and data analyst is often murky. Unfortunately, that can make it difficult to find the right job to kickstart your data science career. We broke down what backgrounds and skills are required to land a job as a data analyst so that you can jumpstart your career.
What educational background do you need to land a job as a data analyst?
Many data analysts have bachelor’s or master’s degrees in data science, math, statistics, economics, computer science, and other quantitative fields. Some even have Ph.Ds in statistics-heavy fields like anthropology, sociology, and psychology. Others come from fields like cognitive science, biology, and the physical sciences.
In general, data analysts must have a strong grasp of statistics, probability, programming, and other mathematical concepts. Data analysts who eventually want to land a data science job need to work on advancing their skills in machine learning and algorithms.
Many aspiring data analysts jumpstart their careers by interning at major tech companies, freelancing for startups, enrolling in a data science bootcamp, or completing other side projects. These help them to build a standout data science portfolio and add new experiences to their data science resume. If you’re looking for tools to help you advance your skills, check out the best resources to learn data science.
What differentiates data analysts from data scientists and business analysts?
Data analysts have overlapping responsibilities with both data scientists and business analysts. Data scientists generally have advanced skills in math and computer science. On the other hand, business analysts usually have backgrounds in economics, finance, and other related fields. Typically, they make decisions based on reports from data analysts. This means that data analysts need to have business acumen, as well as a good sense of how to put complex mathematical theories into practice.
In smaller companies and startups, as well as major tech companies, data analysts work closely with data scientists and business analysts. In fact, some companies refer to data analysts as junior data scientists. Working under more senior data scientists, many data analysts learn what data scientists do. Then, they pick up the necessary skills and tools to land a job as a full-fledged data scientist or a data analytics manager themselves.
To help you understand the skills required to land a job as a data analyst, we have unpacked the role’s various responsibilities. This will let you discover what data analysts do on a daily basis.
Collect, process, and create data sets
Before data analysts can solve business problems, they collect data and create data sets. In general, they begin by writing queries using tools such as SQL. This allows them to stockpile key data from a wide range of databases. Initial datasets are often imprecise, so data analysts are responsible for cleaning the data. Besides checking for glaring mistakes, they make sure that the datasets’ variables are consistent. Plus, they double check that the data is relevant and eliminate any blatant outliers. Data analysts know that there is a ton of information available. So, they are responsible for picking and choosing the best data sets for solving their company’s most pressing challenges. More junior data analysts focus on using existing tools, systems, and data sets. Alternatively, experienced data analysts are charged with teaching others how to use the data-collection system.
Relevant tools: SQL, NoSQL, MySQL, Hadoop, Apache Spark, Geospatial, Databases, Redis, MongoDB, Business Intelligence tools
Coordinate with teammates to solve business problems
Data analysts use probability theory, statistical modeling, data visualization, predictive analytics, and other methods to help companies understand past performances and to predict future trends. They use diagnostic analytics to understand why something happened and whether or not that outcome was positive or negative. To forecast what will happen, they use predictive analytics. Then, they can offer recommendations based on local, national, and global trends.
Besides helping companies understand their own revenues, sales, and website traffic statistics, data analysts provide insights that are relevant to the industry as a whole. They think about what will happen to the industry and what the company should do about it. Data analysts help companies decide which products to develop, markets to enter, investments to make, and customers to target. They use data to assess risks so that companies can make informed business decisions.
Relevant tools: Scrum, Kanban, Waterfall, CRISP-DM, JIRA, Trello, Microsoft TDSP, Rescoper, Priority Matrix, Zangi
Explore and model data
After collecting their initial data sets, data analysts use programming languages and other software to analyze the information. They consider variables such as age, gender, geography, time, and marketing campaign strategy. With this information in hand, they identify how to improve data collecting processes, recommend system modifications, and set up an infrastructure.
While some data analysts use machine learning, algorithms, and other advanced data science tools to develop predictive models, most use simpler mathematical concepts like clustering, classification, and regression to identify trends and patterns in the data sets. When it comes to predictive analysis, most data analysts leave the heavy lifting to the team’s data scientists, who use a variety of methods to ensure that the data is actually predictive. Often, data analysts are charged with fixing coding errors and other data-related problems.
Relevant tools: Informatica, RapidMiner, R, Java, Python, C++, CSV, Microsoft Excel, Google Sheets, Google Analytics, Google Tag Manager, Impala
Create visualizations, communicate results, and propose solutions
Once they have created their data sets and conducted their initial analysis, data analysts share their results by building spreadsheets, graphs, charts, and other data visualizations. Their results need to be clearly labeled and visually striking so that even team members without strong technical backgrounds can understand and use the results.
Like data scientists, data analysts create both internal and client-facing reports. They need to communicate their findings and suggestions to clients, product managers, executives, and other stakeholders, including digital marketers, sales people, developers, and designers. Looking at the data trends and results, data analysts propose cost-effective solutions, such as investing in a different market or adding new features to existing products.
Relevant tools: Tableau, Ggplot, MATLAB, Python pandas, TensorBoard, Microsoft Excel, QlickView, Glik
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