10 In-Demand Data Analyst Skills to Get Hired in 2024

Are you an aspiring data analyst who is struggling to enter this field? To stay competitive in the job market, you must possess the right data analyst skills, which are in high demand. With technology constantly changing and new trends emerging, it’s important to stay ahead of the curve.

Data analytics is a rapidly growing field with an increasing demand for skilled professionals who can analyze and interpret complex data sets. Employers are constantly seeking individuals who possess a diverse set of skills to excel in their roles as data analysts.

As we look ahead to 2024, the demand for data analysts is expected to continue rising. To succeed in this competitive field, it’s crucial to cultivate and develop the necessary skills to meet the industry’s evolving needs. In this blog, we will discuss 10 in-demand data analyst skills that will set you apart and help you get hired easily.

What is Data Analyst Skills?

The data analyst role involves particular skills that are the prerequisite for many careers that involve extracting insights from data. These insights are used for various purposes, like improving business strategies, identifying trends in marketing campaigns, or understanding customer behavior. The skills are divided into hard and soft skills.

What are the Skills Required for a Data Analyst?

Here are the technical or hard data analyst skills required:

• Excel
• SQL (Structured Query Language)
• Python
• R Programming
• Data Visualization
• Statistics
• Data Wrangling
• Data Cleaning
• Machine Learning
• Data Analytics Tools

Let’s take a look at all the top data analyst skills mentioned above one by one.

i. Excel

While Excel might not be the fanciest tool in a data analyst’s toolbox, it’s a common starting point for many. Here’s why:

• Data Manipulation: You can use Excel to sort, filter, and organize data to prepare it for analysis.
• Basic Calculations: Excel has a wide range of formulas and functions for calculations, data summarization, and basic chart creation.
• Simple Visualizations: While not as sophisticated as dedicated data visualization tools, Excel allows you to create charts and graphs to represent data trends. However, it can be limiting for complex visualizations.

ii. SQL (Structured Query Language)

SQL (Structured Query Language) is a fundamental skill for data analysts because it allows them to efficiently extract and manipulate data stored in relational databases. Here’s why SQL is so crucial for data analysts:

• Data Access: SQL is the primary language for communicating with relational databases, which are the backbone of storing and organizing vast amounts of data. Without SQL, analysts would struggle to access the data they need for analysis.
• Data Retrieval: SQL provides powerful commands to retrieve specific data from tables based on various criteria. Analysts can filter data based on conditions, sort it in different ways, and even combine data from multiple tables.
• Data Manipulation: SQL goes beyond just retrieving data. Analysts can use SQL to modify existing data, delete unwanted records, and even create new tables to structure the data for analysis.

iii. Python

Python programming is a highly valuable skill for data analysts, offering a powerful and versatile toolkit for various tasks throughout the data analysis lifecycle. Here’s a breakdown of why Python is so important for data analysts:

• Powerful Libraries: Python boasts a rich ecosystem of libraries specifically designed for data analysis. Libraries like Pandas and NumPy provide efficient data structures and functions for loading, cleaning, transforming, and analyzing data.
• Flexibility: Python allows you to write custom scripts to automate repetitive data manipulation tasks, saving time and ensuring consistency.
• Data Cleaning: Python empowers you to tackle messy data with ease. Libraries like Pandas offer tools for handling missing values, identifying outliers, and standardizing data formats.
• Exploratory Data Analysis (EDA): Python is excellent for exploring data and uncovering initial insights. You can use libraries like Matplotlib and Seaborn to create various data visualizations to understand patterns and trends.

iv. R Programming

R programming stands as another powerful tool in a data analyst’s arsenal. Here’s why R programming is a valuable skill for data analysts:

• Statistical Software by Design: R was originally created by statisticians for statisticians. It boasts a vast collection of built-in statistical functions and packages, making it ideal for complex statistical analysis.
• Hypothesis Testing: R excels at hypothesis testing, allowing analysts to test assumptions and draw conclusions about their data.
• Statistical Modeling: From linear regression to time series analysis, R offers a comprehensive suite of tools for building and evaluating statistical models to uncover relationships within data.

v. Data Visualization

Data visualization is a crucial skill for data analysts, acting as a bridge between complex data and clear communication. It’s about transforming raw data into visual representations that effectively tell a story and uncover insights. Here’s why data visualization is essential for data analysts:

• Highlighting Key Findings: Visualizations can be used to focus attention on the most important takeaways from the data analysis. By emphasizing key insights, analysts can ensure their message resonates with the audience.
• Data Storytelling: Data visualization goes beyond just presenting data; it’s about storytelling. Through visuals, analysts can craft a compelling narrative that showcases the data’s significance and its implications.
• Identifying Patterns & Trends: Visualizations excel at revealing patterns and trends within data that might be difficult to spot in spreadsheets or tables. This allows analysts to identify areas for further investigation or make data-driven decisions.

vi. Statistics

Statistics is a foundational skill for data analysts. It equips them with the tools and techniques necessary to analyze and derive insights from data effectively. Here are some key statistical skills required:

• Descriptive Statistics: Summarize data using measures like mean, median, and standard deviation.
• Inferential Statistics: Draw conclusions about a larger population based on a sample (e.g., hypothesis testing).
• Correlation Analysis: Identify relationships between variables (e.g., is there a correlation between customer age and purchase amount?).

vii. Data Wrangling

Data wrangling involves collecting, cleaning, and transforming raw data into a usable format. This skill is crucial because real-world data is often messy and requires preprocessing before analysis. Here are some key data wrangling skills that data analysts should possess:

• Data Collection: Ability to gather data from various sources including databases, APIs, files (CSV, Excel, JSON), web scraping, etc.
• Data Integration: Capability to integrate and combine data from multiple sources or databases while handling data schema mismatches and resolving conflicts.
• Data Transformation: Skills to manipulate and transform data such as reshaping data frames, creating new variables, merging datasets, and pivoting data for analysis.

viii. Data Cleaning

Data cleaning, a core part of data wrangling, is essential for data analysts. Here are some key data-cleaning skills for data analysts to focus on:

• Data Quality Assessment: Being able to identify and assess issues with data quality. This includes spotting inconsistencies, missing values, outliers, and errors in formatting.
• Handling Missing Values: Missing data is a common problem. Data analysts should be adept at techniques for handling it, such as deletion (if appropriate), imputation (filling in missing values with estimates), or carrying forward/backward values from existing data.
• Identifying and Fixing Errors: This involves spotting typos, incorrect data entries, and other inconsistencies. It may require referencing external sources or domain knowledge to verify accuracy.

ix. Machine Learning

Machine learning skills are essential for data analysts to extract valuable insights, build predictive models, and drive data-driven decision-making. Here are key machine-learning skills that data analysts should possess:

• Understanding of core ML concepts: This includes familiarity with supervised learning (classification, regression) and unsupervised learning (clustering, dimensionality reduction) techniques.
• Basic knowledge of common ML algorithms: Having an idea of how algorithms like linear regression, decision trees, or k-means clustering work can help analysts interpret results generated by data scientists or choose appropriate pre-built models for simpler tasks.
• Familiarity with machine learning libraries: Libraries like scikit-learn (Python) or caret (R) provide tools for building and deploying basic ML models. Understanding these libraries can empower analysts to automate tasks and gain more insights from data.

x. Data Analytics Tools

Proficiency in data analytics tools is a crucial skill for data analysts. These tools facilitate various stages of the data analysis process, from data acquisition and wrangling to analysis, visualization, and communication of insights. Here are some of the key tools that you must be familiar with:

• Big Data: When dealing with massive datasets, big data tools come into play. These tools are designed to handle the storage, processing, and analysis of datasets that are too large or complex for traditional data processing methods. It includes Apache, Hadoop, Spark, Kafka, etc.
• Data Visualization Tools: Data visualization tools help turn complex data sets into easily understandable and interpretable visuals, such as charts, graphs, and maps. Some popular data visualization tools include Tableau, PowerBI, Plotly, etc.
• Other tools: In addition to big data and data visualization tools, there are several other software applications that data analysts use frequently. These include SAS, Knime, Google Analytics, etc.

Final Take

The competition is cut-throat these days, so having the right skills is essential. Careervira can help you gain in-demand data analytics expertise through some of the best online data analytics courses. Learn from industry experts, master the necessary tools, and get ready to launch a successful data analytics career.