Essential Data Analyst Skills You Need to Possess in 2023 – IQCode

Data Analyst Technical Skills

As a data analyst, there are technical skills that you must possess to excel in your profession. Below are some of the technical skills that every data analyst should learn:

1. Visualization of Data: Data visualization skills help you to produce graphical representations and visual summaries of data. Some popular tools used for this include Power BI, Tableau, and Excel.

2. Cleaning of Data: Data cleaning involves removing errors and inconsistencies from the data. You can use tools like OpenRefine, Python, and Excel for data cleaning.

3. Technical Coding Languages: You need to learn at least one coding language to become a data analyst. Here are three of the most popular you could use:

PYTHON: One of the most popular programming languages for data analysis, Python is flexible, easy to learn, and has many useful libraries such as pandas, numpy, and matplotlib.

R PROGRAMMING: R Programming is a popular language used for statistical computing and graphics. It is ideal for data visualization

MATLAB: MATLAB is used for scientific computing and is useful when dealing with large datasets

4. Databases Knowledge: Data analysts should have knowledge of databases to work with SQL.

5. Machine Learning and Artificial Intelligence: Understanding machine learning and artificial intelligence concepts will help you to make predictions based on data.

6. Mathematical Knowledge: Statistical thinking is a crucial aspect of data analysis. You need to understand the mathematical concepts used in data analysis such as correlation, regression analysis, and probability.

7. Microsoft Excel: A data analyst must be proficient in Microsoft Excel, as it is commonly used for data manipulation and visualization.

In summary, technical skills are essential as a data analyst. The more technical skills you possess, the more versatile and valuable you become in your profession.

Data Analysis: The Key to Your Career in the Data-Driven World

Data analysis is the process of working with data to identify usable information, inform decisions, and draw conclusions. In today’s data-driven world, it’s a critical skill for professionals across industries. With the demand for skilled data analysts on the rise, now is the time to invest in your skills.

The global Big Data and business analytics market has grown rapidly over the last several years, creating significant opportunities for those with data analysis skills. While traditional master’s programs are an option for skill development, data analytics boot camps offer a cost-effective and efficient way to learn the necessary technical and soft skills.

Whether through a boot camp or other program, developing a strong set of data analysis skills will be key to building your career in this field. So take the step today to invest in your future as a sought-after data analyst.

Who is a Data Analyst?

A Data Analyst is responsible for using technical skills to analyze data and report findings that help recipients better understand a situation. The findings are used to inform key decisions and actions. Data analysis is not limited to reporting since it typically requires additional analysis to provide specific recommendations. A Data Analyst’s daily routine involves collecting data from a company database using SQL skills, analyzing that data using programming abilities, and reporting their findings to a larger audience. The responsibilities of a Data Analyst include:

  • Extracting data from primary and secondary sources using automated tools
  • Fixing code errors and removing corrupted data
  • Creating and managing databases and data systems while rearranging data into a usable format
  • Analyzing data to determine its quality and meaning
  • Using statistical tools to analyze and interpret patterns and trends in large datasets
  • Giving numerical value to critical business functions to measure and compare business performance across time
  • Analyzing local, national, and worldwide trends that affect the organization and its industry
  • Preparing management reports that include trends, patterns, and predictions based on important data
  • Identifying process improvement opportunities, proposing system upgrades, and developing data governance strategies with programmers, engineers, and management executives.

Being a Data Analyst is a rewarding career with good pay. By working as a Data Analyst, you’ll gain skills that can help you advance to more sophisticated professions like Data Scientist.

Key Technical Skills for Data Analysts

Data analysts are professionals who work with data to extract valuable insights. They use various technical tools to analyze large data sets and make informed decisions based on their findings, including data cleaning, data quality assessment, and report preparation. Therefore, having a well-rounded toolkit of technical skills is essential for performing these tasks effectively. Here are some key technical skills that a data analyst should possess:

Understanding Data through Visualization

Data visualization shows data findings to non-analytical professionals using graphics and visuals. It facilitates easy comprehension of complex data concepts and identifies trends in quick succession. Data analysts can leverage this technology to present significant insights to decision-makers for informed decision-making. Innovative data visualization techniques foster exploration, deep analysis, and quicker solutions. According to LinkedIn Learning, data visualization is one of the most in-demand skills for recent graduates, as it enables them to deliver data stories and analyses that drive impactful business decisions.

Clean Data for Success

Cleaning data is crucial to achieve success, as any fan of Marie Kondo would agree. It is a vital process when building a machine learning model, and it remains a significant component of a data analyst’s daily routine. “Better data trumps fancier algorithms” is a basic truth in machine learning. Data scientists spend around 80% of their time cleaning data instead of experimenting with machine learning models. It’s simple: properly cleaned datasets can deliver astounding insights, while unclean data may produce misleading patterns and lead to inaccurate conclusions. Data analysts must have good data cleansing expertise as a prerequisite for their qualifications.

Popular Programming Languages

Here are some widely used programming languages:

function printLanguages() {
const languages = ['JavaScript', 'Python', 'Java', 'C#', 'Swift'];
languages.forEach(language => console.log(language));

The above code will list out some of the most popular programming languages used in the industry.

Remember to always stay up to date with the latest trends

What is MATLAB?

MATLAB is a versatile programming language and numerical computing environment used for implementing algorithms, manipulating matrices, and plotting data. It is popular for big data analysis due to its ability to reduce pre-processing time and quickly organize and visualize data. It also allows the running of machine learning models on different platforms. While not a necessary skill for data analysts, familiarity with MATLAB can give an edge when seeking employment opportunities.

R Programming for Data Analytics

R is a widely used programming language for data analytics and was ranked fifth in the top programming languages survey by IEEE Spectrum in 2019. Its syntax and structure are optimized for analytical tasks with built-in data organizing commands. Businesses prefer R for managing complex and large datasets. Aspiring data analysts should prioritize learning R because of its popularity and functionality.

Why Python is the Best Choice for Aspiring Analysts

Python is the clear winner for aspiring analysts. This high-level programming language has a plethora of specialized libraries that are specifically relevant to AI. The AI software market is growing rapidly, and Python’s suitability for AI development makes it a must-have skill for data analysts. Aspiring analysts should consider learning ancillary Python applications such as Pandas and NumPy to expand their Python knowledge.

Understanding Databases for Data Analytics

In the world of data analytics, knowledge of databases is essential. There are two types of databases, SQL and NoSQL, with SQL being the most widely used. Despite its age, SQL remains the standard method for querying and manipulating data in relational databases. While it’s important to learn general-purpose languages like Python or R, failing to learn SQL can make getting a job in data significantly more difficult.

MySQL is a branded SQL version that provides opportunities to learn more about relational database management systems. NoSQL databases structure data differently from SQL databases, and there are many different frameworks to choose from. MongoDB is a popular choice for those looking to explore NoSQL databases.


Knowing how to use machine learning can help job seekers stand out in the data analytics market. AI-powered systems analyze data and predict consumer preferences, product development, and marketing channels. A basic understanding of machine learning tools and concepts can make analysts more competitive during job searches.

Mathematical Skills for Data Analytics

Advanced mathematical skills are vital for data analytics. Many data analysts opt to major in mathematics or statistics during their undergraduate years to gain a better understanding of the theory driving real-world analytical practices.

Linear algebra and calculus are two specific branches of mathematical research that are essential for analytics. Linear algebra facilitates performing vector, matrix, and tensor operations in machine learning and deep learning. Calculus is used to construct the objective/cost/loss functions that provide instructions to algorithms on how to achieve their goals.

// Sample code for vector operation in Python
import numpy as np
a = np.array([1,2,3])
b = np.array([4,5,6])
c = np.add(a,b)

In the above code sample, we utilize the NumPy library to add two vectors together and print the result.

Microsoft Excel: The Widely-Used Spreadsheet Platform

Despite the availability of more advanced technology, Microsoft Excel is still widely used by 750 million people worldwide, with “Excel skills” listed in many job postings. Although it may be clumsy in comparison to other platforms, Excel is evolving with features such as VBA, which allows the creation of macros for improved automation of repetitive tasks such as accounting and project management. The Analysis ToolPak, designed for statistical and data analysis, generates output tables and charts to display results quickly. Excel may not be the most advanced platform, but it remains a reliable tool for many organizations.

Soft Skills for Data Analysts

While technical skills are essential for data analysts, they are not the only factor for a successful career. Being a fantastic data analyst on paper is not enough to ensure employment. For ambitious data analysts, having non-industry-specific soft skills is just as important. Among these, critical thinking and communication are crucial for success.

Developing Critical Thinking Skills for Data Analytics

Analyzing data goes beyond just observing it. As a data analyst, it is crucial to comprehend the data and its implications. Enhancing your critical thinking skills enables you to derive useful information and insights from the data.

Developing these skills involves challenging yourself to think beyond just processing and to consider the significance behind the patterns you observe. For instance, when analyzing data, evaluate what the outcomes say about the project or task being evaluated and what corrections can be made.
Therefore, for a successful career in data analytics, you must develop and enhance your critical thinking abilities as an essential aspect of your training.

Bilingual Communication: A Key Skill for Data Analysts

As a data analyst, it’s not enough to simply identify patterns and insights from data – you must also be able to communicate them effectively to both technical and non-technical stakeholders. To be successful, it’s crucial to be proficient in both languages. This means being able to explain complex technical concepts to fellow data professionals while also providing clear and concise explanations to business-oriented decision makers. Failure to communicate effectively could hinder your performance as a data analyst.

How to Build Data Analytics Skills for a Career

If you’re interested in a career in data analytics, it’s crucial to have the right skills. Look into local academic programs such as undergraduate and master’s degree programs, or consider enrolling in a data analytics boot camp for a faster and more affordable option. These educational opportunities will help you gain a comprehensive understanding of your chosen technology discipline. Start building your foundation of data analysis skills today and open up a world of opportunities in the field.

Frequently Asked Questions (FAQs)

Q: Is it hard to become a data analyst?

A: The skills needed to become a data analyst are not difficult to learn, but subject expertise and technical skills to query and generate insights from data are required, making it a never-ending learning process.

Q: What does a data analyst do?

A: A data analyst analyses data and acts as a gatekeeper for an organization’s data, ensuring that all stakeholders understand the information and can use it to make smart business decisions. It’s a technical position that requires a bachelor’s or master’s degree in analytics, computer science, mathematics, or modeling.

Q: How can I become a data analyst?

  1. Earn a bachelor’s degree in a subject with statistical and analytical skills, such as math, computer science, or related fields.
  2. Learn the basics of data analytics.
  3. Consider certification.
  4. Get your first job as a data analyst.
  5. Advance your career with a master’s degree in data analytics.

Q: What is data analyst eligibility?

A: To work as a data analyst, a bachelor’s degree is required for most entry-level roles. Relevant fields include Finance, Economics, Mathematics, Statistics, Computer Science, and Information Management.


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H3 Title: Additional Resources

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Here are some additional resources related to data analyst interviews, resumes, and salaries:

– Data Analyst Interview Questions: This link contains sample interview questions for data analysts.
– Data Analyst Resume: This link provides tips for creating a strong data analyst resume.
– Data Analyst Salary: This link discusses the typical salary range for data analysts.

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