Data Analysts are skilled practitioners that use their technical knowledge to carry out the tasks related to cleaning, analysing, interpreting, and displaying data by using various approaches and intelligence business tools for deeper analysis.
Data analysis is one of the highest in-demand jobs in industries, like business, finance, criminal justice, science, medicine, and government. This demand for Data Analysis Skills continues to disrupt the Job Market as Data Science, and Machine Learning advances to change the world for the better.
Most data analyst job roles typically involve data extraction, analyzing, interpretation and visualization, but the attractive salary packages and perks reflect only the critical need for this job role, which keeps growing.
You may already have some of these skills and others might be new to you. Either way, you have several options for developing your data analyst skill set. Your learning efforts will give you the competitive advantage you need when it comes time to apply for jobs.
In this article, we’ll discuss data analyst skills you should work to showcase in your data science portfolio. Let take a closer look at all the essential skills related to an entry-level data analyst job role and discuss what you need to learn to support your career prospects.
Database (SQL and NoSQL)
Data Analyst extract and analyze data stored in databases.
Companies use different database management systems according to their requirements. As you’re growing your knowledge, it can be super helpful to gain a deep understanding of how databases work.
You will face questions about SQL and NoSQL databases in your data analyst interview. You’ll also be required to demonstrate your database knowledge to collect and gather data for your projects.
Learning the essential concepts will help you build analytical skills to perform analysis.
SQL is easy to learn, even if you don't have any background in programming or computer science.
As a Data Analyst, you’ll should be able to write SQL queries to handle a variety of data analysis tasks.
- Learn the fundamentals of SQL
- Learn to extract data, join tables together, and perform aggregations.
- Learn how to filter, sort and summarize data to solve problems.
- Learn to do more complex analysis and manipulations using sub-queries, temp tables, and window functions.
You can develop SQL fluency, even if you have no previous coding experience, with these SQL for Data Science courses from world-class data science educators.
You will work with NoSQL databases in your role as a data analyst. NoSQL databases are suitable for dealing with big data over SQL because of their flexible schema requirements and modern data architecture.
You must be highly equipped to carry out various and even complex data analysis tasks from non-relational databases. Therefore, learn first the fundamental concepts of the different NoSQL databases and then build familiarity with data analysis tools, including programming, to stay at the forefront.
And here’s what you need to learn...
- Document-Based Database
- Key-Value Database
- Wide Column Based Database
- Graph-Based Database
Interested in this skill? Check out NoSQL Concepts by DataCamp
Statistics is at the core of sophisticated data science models and machine learning algorithms, capturing and translating data patterns.
The applications of Statistical methods in data analysis primarily involve the collection, description, analysis, and inference of conclusions from data.
With a firm foundation in descriptive and inferential statistics, you'll be able to:
- Use statistical formulas and computer algorithms to identify patterns, relationships and trends within data.
- Use statistical techniques to find actionable insights by gathering and analyzing large amounts of structured and unstructured data.
- Avoid logical errors, fallacies and even biases into your analysis.
- Provide accurate and trustworthy results
Data analysts use statistics to gather, review, analyze, and draw conclusions from data.
Learn Essential Statistics Concepts to understand fundamentals. We have created a few study guides that explain the most basic concepts.
- Essential Statistics for Data Science (Beginner's Study Guide)
- Probability and Statistics for Data Science Courses (Non-programmers)
If you’ve already picked up some programming, learn specific ways to apply your skills with learning resources.
Statistical programming languages, like Python and R, enable you to perform advanced data analyses and predictive analytics on large data sets.
As a data analyst, you'll need the programming skills to clean, analyze, and visualize large data sets more efficiently.
Both programming languages do a great job with data cleaning, wrangling, and analysis.
Python is an easier language to learn and popularly used by data scientists, data engineers, and researchers.
You need to be well-versed in working with the libraries that provide functionalities for data cleaning, preparation, analysis, exploration, etc.
You should be able to work with the following packages:
- Numpy and SciPy: Fundamental Scientific Computing
- Pandas: Data Manipulation and Analysis
- Matplotlib: Plotting and Visualization
- StatsModels: Statistical Modeling, Testing, and Analysis
- Seaborn: For Statistical Data Visualization
Learn Python if you've never written code before from notable educators.
- Become a Data Analyst (Nanodegree) – Udacity
- Data Analyst in Python – Dataquest
- Data Analyst with Python – DataCamp
— R Programming
R programming is an open-source language used for statistical computing or graphics. It is perfect for statistical analysis and data mining.
R programming is in many aspects considered a better option for data analysis based on scalability, performance, integration, etc.
It is widely being used for data analysis and statistical modeling.
You should be able to work with the following packages:
- RMySQL and RSQLite: Learn to use database drivers for reading and analyzing data from a database.
- stringr: Use tools that work with character strings and regular expressions.
- dplyr: Learn create and use functions for summarizing, connecting and rearranging datasets.
- lubridate: For working with dates and times across various periods.
- ggplot2: For creating visually appealing plots and graphics.
- rgl: enables three-dimensional, interactive visualizations with R in which you can rotate and zoom in on parts of a visualization.
- randomForest: For unsupervised learning.
- caret: For training, classification and regression models.
- shiny: For creating web apps.
- ggmap: For spatial data and integrates with ggplots.
- xts: For working with time series datasets.
- XML: For working with XML documents.
Interested in learning R? Check out the following career tracks.
Extraction, Transformation, and Loading (ETL)
When working on projects, data analysts train the machine learning model for predictive analytics to solve a business problem. The ETL process supports model development.
There are three unique processes in extract, transform, load.
Extraction: Data Analysts start by extracting raw data from selected sources in several formats, such as relational databases, XML, JSON, and others.
Transformation: This process involves standardizing, cleaning, mapping, and augmenting the data before loading it into the database.
Loading: This last process involves delivering data for analysis or sharing to make it available to other stakeholders.
Data Analysts are expected to have the knowledge of ETL tools to automate the extraction, transforming, and loading processes for consolidating data from multiple data sources or databases.
The knowledge of essential math is important for data analysts. The main reason data analysts need math is that high-value predictions improve decision making with an awareness of how choices affect outcomes.
Linear algebra and calculus are frequently used in data analytics.
- Linear algebra has applications in machine and deep learning for predictive analytics, where it supports vector, matrix, and tensor operations.
- Calculus is used to predict function and build the functions that train algorithms to accomplish their objectives.
Essential Math skills come in handy for solving complex problems. They are so critical that most companies require a functioning knowledge of statistics, graphing, algebra, probability, optimisation techniques, and more.
You can learn the basics you need to learn in our basic math for data science guide for beginners. It's suitable for a data analyst's career path.
Machine learning is important for accurate predictions in data science. It supports data analysis process to generate better insights to encourage stronger decision-making.
You should have a basic understanding of the machine learning concepts, algorithms, and tools to stand out from the crowd.
Data analysts need to know how to build predictive analytics solutions that provide descriptive and projective results from data. These solutions are developed using existing data to generate results from future data.
Data analysts need to understand the vast range of ML algorithms, including:
- Supervised Learning Algorithms
- Unsupervised Learning Algorithms
- Reinforcement Learning (For Finance)
The more data we process using machine learning algorithms, the more profitable it becomes to generate insights with actionable value.
Learn Machine Learning: Machine Learning Intro from Dataquest is one of the best course to learn the machine learning techniques with Python.
Data visualization has revolutionized the way data analysts operate with data.
Data Analysts use eye-catching, interesting charts and graphs to present their findings in a clear and concise way to assist business decision-makers at a glance.
Data is gold and to evaluate its value, big data is combined with machine learning to create predictive analytics that bring the value of the data to light.
It's not a soft skill, it's rooted and grounded in deep technical research. You must also be able to explain your findings in a way that is understandable even to those who don’t have enough technical knowledge.
Some of these top data visualization skills for data analysts include:
- Ability to working with large datasets
- Critical thinking and statistical intuition.
- Understanding target audience, demographics, and interests.
- Using clear and concise language, including elements to preserve attention
- Data Storytelling.
— Data Visualization Tools
These data visualization tools can increase the efficiency of data analysts.
- Tableau: Tableau is considered an industry-standard analytics tool. It is easy to learn, and it has a clean and intuitive user interface.
- QlikView: QlikView is a Business Intelligence software that allows you to develop guided analytics applications and dashboards.
- Datawrapper: It is an online data visualization tool that can be used to create charts, maps, and tables in various contexts. It is easy to use, and very user-friendly.
- PowerBI: It is good for reporting, self-service analytics, and predictive analytics.
- Plotly: You create a visualization of a dataset in less time with Plotly. It is easy to use and you can create beautiful graphs, charts and maps.
- Sisense: You can create extensive and informative dashboards with Sisense to understand your data better.
- Excel: You can use Microsoft Excel to analyze statistical, scientific, and economic data for a wide range of purposes.
For data analysts, learning to work with these data visualization tools is important.
Learn Tableau: It is one of the most popular data visualization tools in the data science industry.
There are many ways you can build these skills to transition into an analytics career. It can be really helpful to take advantage of courses, books and other free resources.
If you are serious about data science, including SQL, Statistical Programming, and Data Visualization, we've got a few practical resources for you.
- How to build a Strong Data Science Portfolio (Beginner's Guide)
- 7 Data Scientist Skills to Get You Hired in 2022
- Essential Statistics for Data Science (Beginner's Guide)
- Basic Math for Data Science (Study Guide)
FAQs about Data Analyst Skills
The learning resources recommended in this skills guide are perfect for beginners to grow technical skills step by step.
How to build a Data Analyst Portfolio?
You can build a portfolio while learning to showing that you have the right set of skills for the job.
- Learn how to Build a Data Science Portfolio
It is an imprint of your technical skills to influence decision-making.
What skills does a data analyst need?
Data Analysts use a combination of technical skills and workplace skills in their analytical work. Some of these skills are specific to the world of data science and machine learning, while others are more general. Chances are you already have some skills that will transfer into a new career in data analytics.
Is Programming mandatory for Data Analyst?
Yes. To work as a data analyst, you need high-level proficiency in statistical programing. Python and R are the two dominant languages in the field.
Is Excel good for Data Analyst?
Excel is well-used among data practitioners and it has its own programming language VBA.
Are Data Analyst Skills in Demand?
Yes, the skills mentioned in this article will help you land the entry-level job as a data analyst.
Does Data Analyst need math?
Yes, you need to be familiar with a linear algebra, calculus, probability, and statistics.
Data analysts are most in-demand people in the world. You can have a distinguished career in data analytics if you build the right skills. By learning and honing these skills, you will increase your chances of getting hired.
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