12 In-Demand Data Analyst Skills You Need to Get Hired

Introduction

What is Data Analytics?

• Data Analytics is the field of collecting, processing, and analyzing datasets to detect underlying trends and patterns that can conclude, make predictions, forecast, and drive decision-making.
• Data Analytics should not be confused with Data Analysis. These terms can look similar but they are not the same. Data Analysis is part of the Data Analytics field. Besides Data Analysis, the Data Analytics field also includes Data Science and Data Engineering.
• So, Data Analysts, Data Scientists, and Data Engineers, all are part of the Data Analytics field and work towards a common goal which is processing the data and making businesses data-driven in their decision-making.

In the following sections, let’s understand what a Data Analyst does and what Data Analyst Skills are required.

What Does a Data Analyst Do?

• Data Analysts deal with enterprise datasets to solve known or tangible business problems by applying various visualization tools, statistical analysis, and basic programming languages.
• Data Analysts collect, process, and analyze data to uncover underlying trends or monitor KPIs/metrics to help business managers understand and solve problems. They bring technical expertise to examine the data, derive insights, and present it in ways to help businesses and organizations make better decisions.
• For example, a Data Analyst can collect the sales data for a company and create a dashboard that can help the company to understand various business metrics such as in which areas their sales are improving or declining, which product is working better, and where they need to focus more to boost the sales and revenue, etc.
• The below diagram shows the typical steps to perform in a Data Analysis project. It starts with defining the business objective, then Data Analysts collect, clean, and analyze the data to interpret results to derive valuable insights.

Top Data Analyst Skills

To build a career as a Data Analyst, you must learn and master several skills.

The skills required to become a Data Analyst can be divided into two categories:

• Technical skills
• Soft skills

In the subsequent sections, let’s discuss these two sets of skills in detail.

Technical Skills Required to Become a Data Analyst

If you are considering becoming a Data Analyst, you need to be proficient in a certain set of technical skills as mentioned below :

Data Visualization

• Visualization is the graphical representation of the data using visual elements such as charts, graphs, etc., which can help convey the information in a way that is easily understandable and pleasant to the eye.
• Data Visualization is the most important skill to have for a Data Analyst. Data Analysts collect the enterprise data stored in disparate repositories and visualize the data to uncover the underlying trends, patterns, etc., and monitor the various business-related metrics. Data Analysts must understand various visualization/dashboard software or tools available such as PowerBI, Tableau, etc. to perform a visual analysis of the data.

Data Cleaning

• Often, Data Analysts have to deal with messy or incomplete data. This data which is unusable at the beginning, needs to be cleaned and transformed into a format that is usable and understandable.
• Data Analysts perform several steps in the Data Cleaning process. This includes discarding irrelevant information, removing duplicate entries, handling or imputing missing values, and filtering outlier values.

MATLAB

• MATLAB is a numerical computing language developed by MathWorks that can be used for developing high-level mathematical solutions such as Fourier Transform, Signal Processing, Matrix Algebra, Data Plotting, etc.
• MATLAB is not widely used in the Data Analytics field but it offers some advantages over other languages such as quick facilitation of Data Cleaning, Transformation, Visualization, etc., faster matrix operations, etc.

R

• R is one of the most popular programming languages used in data analytics. It was initially developed to solve the statistical problem but has now evolved into a complete Data Science ecosystem.
• Dpylr and readr are the most popular libraries used for Data Collection, Processing, and Analyzing. Data Analysts also use the ggplot2 library to plot the data using various visualization methods.

Python

• Python is the most popular and widely used programming language in the Data Analytics field. One of the main reasons for Python’s popularity in this community is its ease of use and simplified syntax, making it easy to learn and adapt for people with no engineering background. Also, you can find a lot of open-source libraries along with online documentation for the implementation of various Data Analysis tasks such as Data Cleaning, Data Manipulation, Data Visualization, etc.
• Given the popularity of Python language in this field, learning it should be on the top for any aspiring Data Analyst. A few of the most common Python libraries used by Data Analysts include :
  • Pandas:
    • Pandas is an open-source Python library designed to deal with data analysis and data manipulation. It is the best available library when it comes to data cleaning and wrangling. Pandas have a lot of in-built functions to explore, visualize and analyze the data in many ways.
  • Matplotlib :
    • Matplotlib is a handy library that provides methods and functions to visualize data such as graphs, pie charts, plots, etc. You can even use the matplotlib library to customize every aspect of your figures and make them interactive.
  • Seaborn :
    • It is an advanced version of the matplotlib library that enables Data Analysts to plot various complex visualization methods such as histograms, bar charts, heatmaps, density plots, etc with a few lines of code. Its syntax is much easier to use compared with matplotlib and provides aesthetically appealing figures.
  • Plotly :
    • This library provides tools and methods to plot interactive figures. Plotly also enables Data Analysts to make interactive dashboards by putting beautiful figures in one place to communicate insights to the business stakeholders.

SQL and NoSQL

• SQL stands for Structured Query Language that is used by Data Analysts to query, update, and manage relational databases and extract data. For years, organizations have been storing their data in relational databases due to their simplicity and ease of maintenance. Data Analysts must have a good understanding of SQL language to interact with databases and collect required data for further analysis.
• In recent years, NoSQL databases such as MongoDB, Cassandra, etc. have gained popularity among companies. NoSQL databases are different from relational databases and can store structured and unstructured data in non-tabular format. Any aspiring Data Analyst should also focus on learning NoSQL skills for the data retrieval from these databases. It can help to stand out from the crowd.

Machine Learning

• Machine Learning is a field in the Computer Science discipline which enables computers to learn the patterns in the data without being explicitly programmed.
• Machine Learning techniques are used to build and develop predictive and prescriptive models. Though Data Analysts do not frequently work on Machine Learning projects. But having a general understanding of relevant Machine Learning concepts can provide you an edge during your interview.

Linear Algebra and Calculus

• Calculus, Linear Algebra, and Probability are the core concepts of any statistical or machine learning algorithm.
• Having strong knowledge of mathematical concepts involved in Data Analytics will help you develop a sound understanding of any statistical or machine learning technique, an essential skill required to become a Data Analyst.

Microsoft Excel

• Excel is widely used among organizations. Based on an estimate, around 750 million people worldwide use the Excel platform.
• Microsoft Excel provides a wide range of features such as in-built mathematical functions, pivot tables, visualizations, etc. It has its language, VBA, which is used to write macros that can help save a lot of time for Data Analysts.
• Excel has a limitation in exploring large data but it is very useful to explore small datasets. But, overall, due to its prevalence in organizations and various useful in-built features, it is a great entry point for the Data Analytics field.

Soft Skills Required to Become a Data Scientist

Along with the technical skills required to become a Data Analyst, they also need to have a set of interpersonal/soft skills. Data Analysts frequently work with business management to communicate their findings and recommendations, so it is essential to have a certain set of soft skills to excel in this field. Let’s get into key soft skills required for a Data Analyst job.

Communication

• As a Data Analyst, you will be required to understand business problems and communicate your findings and recommendations to business management or stakeholders. It is therefore essential to develop strong communication skills for a Data Analyst to be able to communicate effectively.

Storytelling

• Storytelling is the ability to effectively communicate insights from a dataset using narratives and visualizations.
• All the work of a Data Analyst will be useless if it is not understandable to business management. So it is crucial to have storytelling skills for Data Analysts to communicate their findings in the form of oral communication, writing, or using a visualization approach such as dashboards, etc.

Critical Thinking

• Generating valuable insights from the data to answer business questions is not a trivial task, and a Data Analyst must have the ability to figure out where to look for the information in the data as most of the time, valuable insights would not be apparent.
• As critical thinkers, Data Analysts can objectively analyze the data, identify patterns and trends, and extract actionable insights with the data they have in hand. Critical thinking can be developed with experience, by asking the right set of questions, and extensive hands-on practice on diverse Data Analytics projects.

Tips for Learning Data Analysis Skills

Data Analysts use a set of technical and soft skills mentioned earlier to analyze data and extract valuable insights from it. Learning and mastering these skills can set you up for a successful career as a Data Analyst. If you are thinking to build a career as a Data Analyst, here are a few tips for getting started on this journey :

• Prepare a strong learning roadmap or educational plan to learn all the aforementioned skills.
• Focus on building a discipline to learn these skills regularly.
• Keep your focus on understanding the fundamentals first and then gradually improve your skills to learn advanced concepts.
• Sharpen your theoretical skills by practicing them on Data Analysis projects with real-world data. Remember that organizations always prefer practical applications over theoretical knowledge.
• Consider having a certificate in Data Analyst course.

How to Include Data Analyst Skills on your Resume

The first person who scans your resume is often a recruiter who does not have the right technical skills or expertise to evaluate your profile. Some employers also use automatic resume scanning systems to automate the screening process. Thus, it is crucial to put the right set of skills on your resume to make it impactful and relevant for the role. Here are a few tips on how to add your skills to your resume to make it more impactful :

• Have a separate section in your resume to mention your Data Analytics skills.
• Always use bullet points to describe all of your technical and soft skills.
• Make sure to provide some context while describing your skills. E.g. built a dashboard to report sales numbers using PowerBI, wrote Python scripts to scrape the data from websites or APIs, etc.
• Make sure to continuously update your resume as you learn and develop new skills.

Data Analyst Course

If you are thinking of building your career as a Data Analyst, you can check out the Data Science Course provided by Scalar Academy. It includes live online classes, hands-on experience on projects from top companies, 1:1 mentorship from expert Data Analysts and Data Scientists, and secure placement assistance. This is a curated course that comes with an up-to-date curriculum taught by the best instructors. It can help you learn and master all the skills required to become a successful Data Analyst.