Do Data Analyst courses teach Programming Languages?

Data analytics have become a formidable force in tackling huge volumes of data in the workplace in today’s world and is only expected to gain momentum in permeating every industry across a multitude of sectors due to the sheer prowess in providing valuable insights that can drive business expansion. with data analytics coupled with advanced technologies and tools that can help decipher the data, there is a considerable rise in the number of programming languages, tools, and other tactics that can help one hone the necessary analytics skills and reach the top of their game.

Whether you are a novice or an IT professional looking to transition into the data analytics realm, there is a multitude of data analyst courses available that can help you tune your acumen to build the technical expertise and pick up the advanced knowledge and skills that can help kickstart your career as a data analyst.

To be a highly skilled data analyst who can collect, analyze, and manipulate data to draw new insights from it, it is vital for one to advanced statistical and quantitative coding abilities. A data analyst must be proficient in computer languages to undertake statistical analysis. If you wonder whether enrolling in a data analyst course will be advantageous and whether programming languages will be covered, the short answer is ‘yes.’ Continue reading to learn which programming languages you need to know if you want to be a data analyst and whether they are taught as part of data analyst courses.

The top must-know Data Analysis Programming Languages for a data analyst

1.  Python

2.   R

3.  SQL

4.  Java

5.  Scala

1. Python

Python is one of the most frequently used general programming languages and has been used extensively in the fields of data science and analytics since it was first introduced by Guido van Rossum in 1991. It makes it easier to swiftly design and manage data structures, enabling you to examine and work with complicated data sets without difficulty. It also makes it possible to create coding applications and websites quickly. Additionally, Python offers a vast ecosystem of libraries and tools that can help with speedy and effective data processing. Python is a great introductory language for people who are new to the fields of data science and analytics because of how simple its syntax is to learn. You might need to download libraries to use Python, which will lessen the amount of coding needed. Python is a great choice for complex machine-learning applications because it also has libraries like TensorFlow, Pandas, and Scikit-Learn.

2. R

R is a free and open-source statistical computing or data analysis graphical language. It is easier to learn than Python and does not require as many additional libraries. R has several uses in business analytics, including creating beautiful visualizations by identifying patterns in data or developing statistical models.

R offers statistical tools for analytical applications, which is why data analysts utilize it. R is an additional open-source language made to adapt to changes.

3. SQL

SQL, or Structured Query Language, is a sophisticated programming language used to communicate with relational database management systems (RDBMS) as well as to store, process, manage, and manipulate data. It is a popular data science programming language because it allows data analysts to perform activities such as data updating and retrieval in their company’s databases. As a result, it is one of the most important abilities for professionals wishing to develop in their data analysis jobs, and its intuitive syntax is simple to master because it is designed for a specific purpose. Because this expertise is in high demand, a variety of SQL bootcamps teach it.

4. Java

Developed by James Gosling in 1995, Java is a general-purpose programming language that enables programmers to write code once and execute it on any platform that supports Java without the need for recompilation. Regardless of the architecture of the computer, Java applications are frequently compiled to run on any Java virtual machine (JVM). This complex language offers strong functionality for incorporating data science and analytical methods into a codebase. This popular language is a crucial resource for data applications.

Let us now look at a real-world example of an effective Data analytical course that provides programming language training.

The Data analyst master’s Program by IBM

The data analyst master’s program offered by IBM on the digital platform of Simplilearn is an excellent program that can help jumpstart your career as a data analyst. The program, which features masterclasses from IBM experts, helps one to build advanced knowledge in the latest analytics tools and techniques where you will learn to work with SQL databases, the programming languages of R and Python, the art of creating data visualizations, and how to apply statistics and predictive analytics in a business environment.

The curriculum

The curriculum of a data analyst course looks roughly like the one given below:

1.  Business Analytics with Excel

Learn how to use data analysis and statistics to make realistic and data-driven decisions. Master MS Excel, one of the most used Microsoft Office programs, to take advantage of data analytics and improve business decisions in this data analytics boot camp.

2.  SQL

With the SQL Course, you can master SQL, the most popular programming language for relational database management systems, which can help you to successfully begin working with SQL databases and utilize the database in your applications. To manage your SQL database for scalable expansion, learn the basics of basic SQL statements, conditional statements, commands, joins, subqueries, and other functions.

3.  Programming Basics and Data Analytics with Python

Learn the fundamentals of Python to aid with data analysis and manipulation. Learn how to use NumPy’s multi-dimensional arrays for Data Analytics, Pandas’ DataFrame manipulation, the SciPy library of mathematical functions, and Scikit-Learn’s machine-learning capabilities. You will discover how to prepare data for analysis, run straightforward statistical calculations, make insightful data visualizations, forecast future trends from data, and more.

4.  R Programming for Data Science

With the help of this beginning course, learn R programming by learning how to use R data structures, write R code, and construct your own functions.

5.  Data Analytics with R

As R is the most popular open-source tool, learning it is the next step toward becoming a data analyst.

6.  Tableau Training

This Tableau certification course will help you grasp Tableau Desktop, a widely used data visualization, reporting, and business intelligence program. The Tableau course teaches you how to utilize the tool effectively for data preparation, interactive dashboard building, dimension addition, and in-depth analysis of outliers.

The final course in the program requires students to work on  a capstone project, where they will be allowed to implement the skills they learned in the Data Analyst course. Moreover, the program covers important tools like Microsoft Excel, SQL, Python, Tableau, MySQL, R, Power  BI, and NumPy.

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