The high demand for data analysts among businesses of all sizes and from every industry, and the high interest among workers in meeting this demand, inspired Practicum to create our unique Data Analyst program.

Being able to process, analyze, and interpret data is becoming a highly valuable, and highly lucrative, skill. It’s also a good move in terms of career security. Data analysis jobs are expected to grow at a blistering pace: 14.3% over the next 10 years. Now more than ever, we need skilled data professionals who can bridge the gap between business and IT and help make smarter data-driven decisions.

What is a data analyst?

Duties and skills of data analysts

A data analyst is a person whose job is to analyze data and uncover hidden connections and ideas. Data analysts are responsible for converting information into insights and conveying their findings to other people, which requires both technical and soft skills. Data analysis is essential in business, administration, and science.

Data analysis is comprised of collecting and structuring data, forming and testing hypotheses, identifying patterns, and drawing conclusions.

At a minimum, data analysts should be comfortable with SQL (Structured Query Language), the preferred query language for interacting with relational databases (including searching for, adding, and updating data). In addition, data analysts should be familiar with at least one programming language. The two most common languages for data analysts are:

  • Python, a highly popular, user-friendly programming language that includes many data science libraries and frameworks.
  • R, a programming language and software environment for statistical computing and data analytics.

In addition, data analysts should also have a solid background in math, especially probability and statistics. Other useful branches of math for data analysts include linear algebra, calculus, optimization, and discrete mathematics.

Data analysts use these technologies and concepts to transform raw data into valuable business insights. The duties of a data analyst include:

  • Collecting data, forming hypotheses, running experiments, and drawing conclusions.
  • Building reports and dashboards that clearly express their findings.
  • Helping to interpret the results and share their conclusions with their colleagues.

It’s important here to differentiate between data analysts and data scientists.

For more information about data scientist profession, read “What’s Inside: Data Scientist Program.”

Although both work with information, they interact with it and use it differently:

  • Data analysts use their talents to help make smart, data-driven strategic business decisions. Good data analysts should have both technical knowledge and strong people skills that help them communicate their findings to their colleagues, especially non-technical leaders and key stakeholders.
  • Data scientists are much more involved in the technical side of data science, including designing algorithms and models and generating questions to ask. They use sophisticated methods to solve complex problems, and their work sometimes overlaps with software engineering. As such, being a data scientist usually involves more in-depth knowledge of programming and mathematics. Communication is a useful skill for data scientists as well, but data scientists interact more with technical colleagues, rather than the non-technical business side of the organization.

The salary range for data analysts

Data analyst salaries will depend on your industry, your skills, and your level of experience. However, talented data analysts should be able to earn a salary that’s significantly above the average income.

Below are some estimates of median data analyst salaries in the US:

Practicum’s Data Analyst program

The high demand for data analysts among businesses of all sizes and from every industry, and the high interest among workers in meeting this demand, inspired Practicum to create our unique Data Analyst program. We don’t have any official prerequisites — just motivation and a thirst for knowledge.

Many of our students already have jobs, which makes it difficult for them to attend full-time courses. That’s why Practicum’s Data Analyst program is online and asynchronous: you can complete the program at a comfortable pace, preparing for a career in data analysis even as you work in another field.

However, make no mistake: you’ll be working hard throughout the program, and you’ll come out the other end with the foundation you need to be a successful analyst. Practicum’s Data Analyst program adopts a results-oriented approach, encouraging students to learn by applying their knowledge to business cases based on real data.

How is the program structured?

The program was developed by a number of experienced educators and professionals from the Yandex School of Data Analysis, with input from successful tech professionals. We based the syllabus on the skills that real data analysts need in their day-to-day work, as well as on thorough research into the data analysis job market.

We want to make sure that all of our students understand what the program is like and that they have the motivation they’ll need to succeed. That’s why we offer a free 20-hour introductory course packed with information about data analysis and the basics of programming in Python.

Once you’ve completed the intro course, you’ll proceed through a series of modules, each designed to teach you about a particular aspect of data analysis. Instruction will take place through Practicum’s custom-built interactive platform, which allows you to learn, ask questions, and get help from our technical support team 24/7.

You’ll have a week to complete each project, after which a code reviewer will look at it. If your project is not accepted, you’ll have a few days to fix any bugs.

Each module concludes with a project to help reinforce what you’ve learned so far. After completing each project, students will receive a detailed code review from our knowledgeable professionals.

At the end of the program, you’ll receive a professional certificate attesting that you’ve successfully completed the program, as well as an optional final course for help with job placement (see below). Even after completing the program, you’ll have access to the learning platform, so you can brush up on the material as needed.

What do students learn?

Serious content

The full Data Analyst program is six months long. You’ll evolve from beginner to master over the course of 220 hours of instruction.

How to embrace feedback

The students in Practicum’s Data Analysis program learn not only technical concepts, but also soft skills that prepare them for their future careers. This includes accepting feedback through project reviews and receiving help from the technical support team, tutors, community managers, and fellow students.

Project reviews identify places where you excelled, as well as potential areas of improvement. They also reinforce one of the most valuable soft skills in the tech industry: embracing constructive criticism. There are a lot of very talented analysts out there, and if you’re going to be the best you can be, you’ll need to learn how to work with managers and more experienced teammates and accept their advice.

Comprehensive support

We eschewed the classic professor-student lecture format in favor of a strong learning and support system for our students. This includes a support team that’s available 24/7 to answer your technical questions, community managers who handle organizational issues, and fellow students who can help you study, collaborate, and network.

During the program, students also enjoy access to tutors — senior tech professionals with years of experience in the field. Our tutors go through a long onboarding process so they can provide students with top-notch guidance. Their job is to explain course material in greater detail, provide examples, share their own professional experience, and answer questions. Students and tutors communicate through live video sessions, as well as on Slack.

What are the projects like?

As mentioned above, each module ends with a project based on real data to make sure that you understand the material. In addition to these individual module projects, you’ll also complete two more complex ones, as well as a final capstone project, for a total of 12 assignments. All of this work can be incorporated into your professional portfolio when you’re applying for data analyst positions.

Our goal is to prepare students to carry out the responsibilities of working data analysts, which is why we based our projects on real-life data and business use cases. The projects include:

  • Determining how the popularity of different music genres on a streaming app varies throughout the day.
  • Identifying causes of customer churn for a ridesharing app.
  • Comparing an e-commerce website’s sales for different geographical locations.

How do we help students find jobs?

We’re committed to our students’ professional success, and we want them all to reach their full potential. That’s why we offer a two-step support system for our Data Analysis graduates:

  • Career prep: We offer access to an optional career preparation course with weekly assignments that guide you through a variety of tasks, from writing your resume and building a portfolio to networking and interviewing.
  • Career acceleration: After successfully completing the course, our staff will help you identify job opportunities, network, and distribute your resume. If you have difficulty finding a job, our staff will offer personal assistance.

Check out our top-rated program here.


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