How To Get a Job In Data Analytics (Real-World Advice)

How to Get a Job in Data Analytics

What does a full-time data analyst career path entail?

Whether you want to become a business analyst, a data engineer or any type of data professional, you'll learn exactly what data analysts do and how to get started in this handy guide.

Step 1: Obtain Relevant Education

• It's not always 100% necessary, but it is beneficial to enroll in a degree program or complete a certificate program to get your career started.

• Some companies require you to have a bachelor's degree, although it's not always a dealbreaker.

• As a data engineer, it's helpful to have a background in a relevant field such as statistics, mathematics, computer science, or economics.

• A formal education provides a strong foundation in data analysis techniques, programming languages, and statistical modeling.

• Courses in data management, data visualization, and database systems are also beneficial.

Step 2: Develop Fundamental Data Analyst Skills

• Mastering technical skills is essential for a data analyst. Focus on learning programming languages commonly used in data analysis, such as Python and R.

• These languages enable you to manipulate and analyze datasets efficiently.

• Understanding SQL (Structured Query Language) for working with databases and extracting information is also a key function.

• Proficiency in programming languages is essential for a data analytics career.

Related Read 1: List of SQL Consultants to Network With.

Related Read 2: Content for Data Practitioners.

Step 3: Gain Practical Experience

• Practical experience is invaluable in the field of data analysis. Seek opportunities for internships or entry-level positions that allow you to apply your data analytics skills in real-world scenarios. These experiences provide hands-on exposure to data analysis tools, techniques, and industry practices.

Step 4: Learn Data Analytics Tools

• Familiarize yourself with popular data analysis tools and technologies such as ETL Tools, Tableau, Power BI, and Excel. These tools facilitate data visualization, data exploration, and data cleansing, enhancing your ability to derive insights from large datasets.

Step 5: Build a Portfolio

• Create a portfolio showcasing your data analysis projects, visualizations, and insights. This is critical for success in your job search.

• Showcase your data analysis projects, visualizations, and insights on platforms like GitHub and post them on LinkedIn.

• Highlight the techniques and data analytics tools you used, along with the outcomes and business impact of your work. A well-crafted portfolio illustrates your problem-solving skills and adds credibility to your resume.

Step 6: Network and Join Communities

• Networking is crucial for landing job opportunities and career advancement in data analytics.

• Join professional communities, get active on LinkedIn, attend industry events, and participate in online forums.

• Engage with fellow data analysts, share knowledge, and seek mentorship opportunities.

• Networking, especially on LinkedIn, is one of the best way to obtain job referrals and develop valuable connections in the field.

• Getting active on LinkedIn is also one of the best ways to grab the attention of hiring managers and connecting with them directly.

Step 7: Apply for Data Analyst Roles

• Once you have acquired the necessary skills and built a portfolio, start applying for entry level data analyst jobs.

• Candidly speaking, you're going to have the most success from networking on LinkedIn and connecting with other professionals in the field, rather than blindly applying to jobs online.

Step 8: Master the Interview Process

• Prepare for data analyst job interviews by practicing technical and behavioral interview questions.

• Demonstrate your problem-solving abilities, critical thinking skills, and data analysis expertise.

• Research the company beforehand and be ready to discuss how you can contribute to their data analytics initiatives.

What Does a Data Analyst Do?

1. Data Collection: Data analysts gather data from various sources, including databases, spreadsheets, APIs, and other data repositories. They may also work with data engineers to ensure the data is properly collected, stored, and accessible for analysis.

2. Data Cleaning and Transformation: Raw data often contains errors, inconsistencies, or missing values. Data analysts clean and preprocess the data, removing outliers, resolving inconsistencies, and imputing missing values. They may also transform the data into a suitable format for analysis.

3. Data Analysis: Using statistical techniques, data analysts analyze the cleaned data to identify patterns, trends, and relationships. They apply various statistical methods, such as regression analysis, clustering, or hypothesis testing, to gain insights from the data.

4. Data Visualization: Data analysts create visual representations of the analyzed data to communicate the findings effectively. They use data visualization tools and techniques to create charts, graphs, and dashboards that convey the insights in a clear and understandable manner.

5. Reporting and Presentation: Data analysts prepare reports and presentations to share the results of their analysis with stakeholders, such as managers, executives, or clients. They explain the insights, trends, and recommendations derived from the data to support informed decision-making.

6. Business Intelligence: Data analysts work closely with business stakeholders to understand their needs and align data analysis efforts with organizational goals. They help identify opportunities for improvement, support strategic planning, and contribute to data-driven decision-making processes.

Business Intelligence vs Data Analysis: What's the Difference?

A business analyst's primary role is to identify and analyze business problems and propose solutions to improve efficiency, productivity, and profitability. Meanwhile, data analysts specializes in collecting, organizing, and analyzing large sets of data to extract meaningful insights.

Key Differences: Read a full breakdown here.

1. Focus: Business analysts focus on understanding and improving business processes, while data analysts concentrate on analyzing and interpreting data to derive insights.

2. Skills: Business analysts require strong communication, problem-solving, and business modeling skills. Data analysts, on the other hand, need proficiency in data manipulation, statistical analysis, and data visualization.

3. Data Handling: Business analysts work with a variety of data types, including qualitative information from interviews and surveys. Data analysts primarily work with quantitative data, extracting insights from structured datasets.

4. Objectives: Business analysts aim to improve business operations, identify opportunities, and address challenges. Data analysts aim to discover patterns, trends, and insights hidden within data to support decision-making.

5. Tools and Techniques: Business analysts utilize tools such as Power BI, process mapping, requirements documentation, and project management techniques. Data analysts employ statistical software, programming languages like Python or R, and data visualization tools.

Data Analytics Career FAQs

What is data analysis?

Data analysis is the process of taking raw data and turning it into valuable insights that drive intelligent decision-making.

What are the main types of data analysis?

1. Predictive Analysis -- Predictive analysis or predictive analytics, is a branch of data analysis that utilizes historical data, statistical algorithms, and machine learning techniques to make predictions or forecasts about future events or outcomes.

2. Descriptive Analysis -- Descriptive analysis or descriptive analytics, is a branch of data analysis that focuses on summarizing and interpreting data to gain insights into past events, trends, and patterns.

3. Diagnostic Analysis -- Diagnostic analysis is a type of data analysis that is used to identify the root cause of a problem. It is a more advanced form of data analysis than descriptive analysis, which simply describes what has happened. Diagnostic analysis uses statistical methods to identify patterns and relationships in data, and then uses this information to address the root causes of data problems.

4. Prescriptive Analysis -- This is one of the most complex types of data analysis. You may have to work with algorithms, machine learning, and computational modeling procedures.

How important is practical experience in data analytics?

Practical experience is highly valuable in data analytics. Internships, personal projects, and data competitions provide hands-on experience and demonstrate your ability to apply analytical techniques to real-world scenarios. Bachelor's degrees are not always necessary if you have demonstrated enough hands-on technical experience.

Although it is possible to become a self-taught data analyst, the journey is not for the faint of heart. Most people would find it easier to enroll in a course, either at university or online. You can also opt for an online bootcamp that will propel you into the field of data analysis in no time.

Should I get formal training, like a master's degree?

It's not always necessary. However, after completing a bachelor's degree, it could be beneficial to enroll into a postgraduate course such as a master's degree in Data Science.

Studying for a master's degree will help you refine the basics of data analysis while also allowing you to practice your skills on real-world projects.

You can also opt for a master's in business analytics if you are looking to move into a business intelligence role.

If you are not ready to go for a formal education yet, you can also become a self-taught data analyst.

Are internships worth it?

They're definitely worth it. Internships offer hands-on experience that allow you to work on real-world projects, collaborate with experienced professionals, and gain practical exposure to tasks like data cleaning, data visualization, business intelligence reporting, and beyond.

It's worthwhile to try and get experience working with datasets from various domains, such as marketing, finance, or healthcare. By exploring different datasets and applying analytical techniques, you can develop a diverse skill set and showcase your ability to extract valuable insights.

Are online courses sufficient for learning data analytics?

Online courses are an excellent way to learn data analytics concepts and tools. However, practical application and real-world experience are equally crucial for developing practical skills.

Which technical skills are needed to be successful?

• Knowledge of programming languages like SQL, R, Oracle, Python.

• Strong mathematical skills to help you collect, measure, organize, and analyze data.

• Proficiency in statistics and statistical packages like SPSS, Excel, and SAS that you will use to analyze data sets.

• Proficiency in handling reporting packages such as Business Objects programming, which include XML, JavaScript, or ETL frameworks, and proficiency in handling databases.

• Experience in using data processing platforms like Apache Spark and Hadoop.

• Proficient knowledge of data visualization tools like Tableau .

• Knowledge of how to create and apply the most accurate algorithms to databases.

• While these are the basic skills you need, you should check out different job descriptions to see what skill sets recruiters are looking for.

What is the average salary for a data analyst?

The average salary for a data analyst in the United States is $82,818 as of May 2023, according to salary.com: https://www.salary.com/research/salary/listing/data-analyst-salary

However, the salary range typically falls between $74,126 and $92,660.

Here are some factors that can affect the salary of a data analyst:

• Education: Data analysts with a bachelor's degree in a related field, such as statistics, computer science, or business, typically earn more than those with only a high school diploma or an associate's degree.

• Certifications: Data analysts with certifications in specific data analysis software or programming languages can earn more than those without certifications.

• Experience: Data analysts with more experience typically earn more than those with less experience.

• Skills: Data analysts with specialized skills, such as machine learning or natural language processing, can earn more than those with more general skills.

• Industry: Data analysts working in certain industries, such as finance or healthcare, typically earn more than those working in other industries.

• Location: Data analysts working in certain cities or states typically earn more than those working in other areas.

What should I expect in a data analytics job interview?

Data analytics interviews typically assess your technical skills, problem-solving abilities, and behavioral competencies. Expect questions about programming languages, statistical techniques, and scenarios where you've applied data analysis to solve problems.

What communication skills do I need as a data analyst?

Your job as a data analyst will involve providing your findings to different stakeholders and decision-makers and convincing them to make business decisions based on the data. As such, you must have excellent communication skills.

To gauge yourself, try explaining a complex concept to a novice until they understand it.

If you can do it, you are good to go --- if you can't, you need to work on your skills.

Here are some ways you can boost your communication skills:

• Start a blog and use it to explain your data analytics results

• Teach your friends about data analytics concepts

• Become a blog writer who specializes in data analytics

• Become active in online forums and discussions on LinkedIn.

• Use GitHub to share and discuss your analysis.

How important is SQL?

SQL is one of the most widely used languages in database management. Learning SQL will give you the skills you need to retrieve, modify and manipulate relational databases. It will also equip you with the skills you need to understand concepts in data analysis and other programming languages.

You will find SQL to be a straightforward and easy-to-learn language. This is why it is recommended that you start with SQL before you move on to other database management languages that might be more difficult to learn.

How important is Microsoft Excel?

Excel spreadsheets are an essential tool for data analysis. You can use Excel to draw meaningful insights such as revenue patterns and marketing trends from any given data sets.

Microsoft Excel also allows you to customize the functions and fields you use for calculations, hence allowing you to easily handle complex data sets.

Excel also comes in handy when you need to segment, analyze and visualize large data sets using Excel spreadsheets.

Learning Excel is the easiest way to begin your data analysis studies when you don't have enough money to pay for classes or courses.

How important is R Statistical Programming?

R is a potent statistical language that will help you perform complex analyses of big data sets.

R has a lot of features that are crucial to data analysis including:

• Ability to allow you to perform multiple calculations using vectors.

• Charts, tables, graphs, and other tools to help you present your analysis with visuals.

• Ability to run code without using a compiler.

R is easy to download and is compatible with both Linus and Windows, making it an easily accessible tool. It is also open source, which allows it to easily integrate with other applications.

How important is Python?

Python is an essential programming language that has many features you can use in data analytics. It is easy to learn and use since it is more readable than other languages and its syntax is very beginner-friendly. Python also runs on all operating systems and integrates well with numerous applications.

Since almost all aspects of data analysis will require the use of Python, you have to learn this programming language. You will need it when mining and classifying data, developing machine learning models, and creating web services just to mention a few tasks.

Should I learn Data Visualization Tools?

Data visualization helps you spot trends, identify outliers, deduce patterns, evaluate your model output, and present your findings in a visually appealing way. Therefore, you must learn as much as you can about data visualization and the tools involved. There are several tools you can use, but Tableau visualization software is the most commonly used.

Should I become proficient in Machine Learning?

Machine learning is becoming increasingly important for the data scientist career path, due to the widespread use of artificial intelligence and predictive analysis.

Although you might never have to work with machine learning, it is important to have a basic understanding of the concepts and tools, especially if you intend to pursue the career path of a data scientist.

If you want to become a data scientist, think about these next steps:

1. Get the necessary education. A bachelor's degree in a related field, such as statistics, computer science, or mathematics, is a good starting point. You can also pursue a master's degree or Ph.D. in data science.

2. Gain experience. One of the best ways to become a data scientist is to gain experience working with data. You can do this by taking on data analysis projects, volunteering with a nonprofit organization, or working as a data analyst intern.

3. Develop your skills. In addition to your education and experience, you will also need to develop your skills in data science. This includes learning how to use data analysis software, programming languages, and machine learning algorithms.

4. Network with other data scientists. Networking with other data scientists is a great way to learn about job opportunities and get your foot in the door. You can attend data science meetups, conferences, and online forums.