Now is the right time to consider a career in data analysis. According to the Bureau of Labor Statistics, there will be an increase of 20% in data analyst jobs between now and 2028. This post will discuss the skills you need to get your first job as a data analyst and how to continue your career.
What skills do you need to be able to hold one of these highly sought-after positions? Data analysts need to be able to use a variety of skills in order to do their job. This includes strong knowledge of data analysis techniques and basic mathematics.
What is a Data Analyst?
Data analysts search through large amounts of data that companies have access to find patterns and trends. Data analysts may use available data to determine which SKUs are the most popular with a particular customer segment or what discounts work best at different times throughout the year.
To become a data analyst you must-have business intelligence and be able to think holistically. The job requires technical skills, but not only. You will also need soft skills such as an understanding of statistics and data visualization.
Also read: Top 20 Data Analytics Tools Used By Experts
What are Skills Required To Become a Data Analyst
Structured query language
SQL is a standard industry tool used to update and communicate data with databases. It can also be used to access and modify data. SQL is used by data analysts because it simplifies the process of storing and retrieving data. It is easy to use and intuitive. Our comprehensive guide to SQL will help you get to grips with SQL if you don’t already know it.
Microsoft Excel
It might surprise industry professionals that Microsoft Excel is used widely by data analysts. Excel is a popular choice because it’s easy to use, lightweight, and can do basic calculations quickly. Excel offers many useful features for data analysts such as functions, pivot tables, and visualizations. Excel is not able to analyze large data sets but can be very useful for small projects and is a great introduction to data analysis.
Programming in Python or R
Data analysts use R and Python as programming languages. Because of their intuitive syntax and powerful data analysis capabilities, they are common in this field.
Some people might wonder if it is better to start with R or Python as your first programming language. Both languages are excellent at data cleaning, wrangling, and analysis. R is slightly ahead in statistical programming languages and reporting. While Python is a bit more difficult to learn for novice programmers, has more options.
It’s impossible to go wrong when choosing R or Python. However, it is important to pick one and stay with it until you are able to understand its capabilities.
Basic Mathematics
Data analysis is a very basic level. It requires a few math skills. No matter how simple it may be to use the libraries in programming languages, every data analyst must be proficient in basic math.
While you don’t require a math degree to work in data analysis you do need to have a bachelor’s degree. However, there are some areas that you should focus your efforts on. This includes linear algebra, probability, and statistics as well as calculus. These are the most important things to focus on and you will find it easy to master all other subjects.
Data Collection
Data collection is the process of obtaining data necessary to perform your analysis. This is the beginning of data analysis and will have an impact on the rest.
Data can be gathered from many sources. You can access public information as well as data your company has collected through its app and website. Data analysts often work with database administrators and colleagues to find the best data sources to solve a particular problem.
Data Cleaning
You won’t always have the data you need to analyze it. You will most likely encounter missing or incorrect information. Data must be formatted and cleaned before it can be used for analysis.
Data cleaning involves several steps. The data cleaning process includes the removal of duplicate entries, filtering outliers, entering missing values using informed estimates, and correcting structural errors. These steps can be assisted by tools like OpenRefine or Trifacta Wrangler.
Data Mining
Data mining, also known as knowledge discovery in data analysis, is also used for data analysis. This stage involves analyzing the data and looking for patterns that might provide insight into business problems.
Data mining begins with setting business objectives. Next, model building and evaluation are performed. You can mine data for patterns using neural networks or machine learning algorithms such as the K–nearest neighbor algorithm.
Data modeling
Data models can be used to visualize the whole data system. These are visual flowcharts that show the entire process.
Also read: Best 10 Amazing and Powerful Data Modeling Tools You Should Know
There are three types of data models.
Conceptual Data Models
These data models give a high-level overview of the system. They include the business entities producing the data and the relationships between them. When the requirements are still being collected, conceptual models are often created in the early stages of a project.
Logical Data Models
Logical data models provide insight into the relationships among entities in a data analysis project. These models show the attributes and relationships of data values as well as the relationship between them.
Physical Data Models
These models are the most abstract. These models show the structures (such as data warehouses or databases) that will be constructed to store the data being analyzed.
Extraction, Transformation, and Loading (ETL).
ETL is a process that transfers data from different sources and structures to a data warehouse. It involves three steps. First, extract data from select sources. Then transform it or cleanse it and finally load it into the database.
Each stage can be used with a variety of tools and techniques. For data extraction, CRMs, websites, ERPs, and other systems are all used. Data cleaning includes validation, filtering, deduplication, and validation. The data transformed is then uploaded to the database.
Machine Learning
Machine learning allows computers to make precise predictions and observations without being explicitly taught. Because it can discover patterns automatically, machine learning is extremely useful in data analysis. This makes the whole process more efficient, productive, and profitable.
Data analysis can be done using a variety of machine-learning approaches and algorithms. Some of the most common are logistic regression, linear regression, principal component analysis, decision trees and naive Bayes.
MATLAB
MATLAB can convert mathematical approaches into algorithms and schemas that can be used in a scalable computing environment. Data analysts use MATLAB for data organization, pattern detection, and implementation of algorithms.
Data Analyst Soft Skills
Critical Thinking Skills
Data analytics requires you to evaluate problems objectively and suggest solutions after analyzing information from many sources. By working on your data science projects, and researching various problem-solving methods, you can improve your critical thinking skills.
Communication
Data analysts must be able to tell stories with their data. They must also have great communication skills, both written and oral.
Data Visualization
An excellent data analyst can both present insights visually and produce them. This post will explain the various types of data visualization, and how to use them.
Also read: What is Data Engineering? Required Skills and Tools
Research
Research is the only way to fill in the knowledge gaps. These data science resources will help you get started.
- Communities
- R-bloggers
- Kaggle
- News
- Wired
- BananaData
Attention to the Details
Data analysts must be detail-oriented to ensure that their code works. This can be achieved by taking your time and being mindful. Once you have mastered these skills, you will be able to move faster.
How to Acquire Data Analyst Skills
An undergraduate degree in computer science is the best way to acquire the skills you need to pursue a career as a data analyst. It will also provide the theoretical foundation you will need to succeed. However, a degree can be costly and take up to four years.
A course or bootcamp can help you accelerate your career in data analysis. Bootcamps are intensive, but they will equip you with the skills needed to work in data analysis. Many bootcamps offer job search assistance.
If you don’t have the budget for a degree, a data analytics bootcamp can be a great option. Self-learners who are proficient in using online resources have the option to do so.
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