Picture this: You’re part of a team that’s building a house. One person is in charge of buying all the building materials and fixtures, but they aren’t familiar with what to do after purchasing them. The house owner has a general idea of what is needed but hasn’t decided how the house will look. The owner also expects the team to suggest different ideas on how to proceed with construction.
This is where you come in.
You’re in charge of ensuring that the purchaser bought enough of the correct kind of materials needed to build the house. You’re also responsible for checking the materials to ensure they’re what’s required and are ready for installation, that none are damaged or defective, and that they are suitable for the house. You present the materials available and suggest how to use them so the homeowner can make decisions about the construction and design of the house.
If you extend the above scenario to the realm of data, you have the role of a data analyst. A person who collects relevant data, ensures it’s understandable, then presents the interpreted data to an organization so it can make sound decisions.
What Is a Data Analyst?
A data analyst identifies, collects, and analyzes big data regarding sales, customer behavior, or market research and turns it into actionable information. They are responsible for cleaning and maintaining the data’s quality and ensuring that it can be interpreted and applied to improve an organization’s decision-making processes.
Given their responsibilities, a data analyst must have well-developed communication skills, including the ability to put together charts, graphs, memos, and written reports, and present these visualizations to stakeholders.
And with more companies using data to drive business decisions, there is high demand for data analysts. The Bureau of Labor and Statistics projects the employment of data analysts to grow 25 percent from 2020 to 2030. According to Glassdoor, the estimated total pay for a data analyst is $95,641 annually, significantly higher than the average salary of a US employee.
So, what steps do you need to take to break into the popular field of data analytics? For tech bootcamp Coding Temple, the journey towards such a career starts with its Data Analytics course.
Coding Temple offers a comprehensive Data Analytics course that can fully prepare you for a data analyst career. Depending on your availability, you can take the program within 12 weeks or 24 weeks.Get in touch with Coding Temple!
A Review of Coding Temple’s Data Analytics Course
- Learning mode: Self-paced
- Tuition: $6,000 for 12-week access; $7,500 for 24-week access
Coding Temple’s Data Analytics course is a self-paced program that can thoroughly prepare students for successful careers as data analysts. The course comprises modules that students can go through at their pace. Depending on what fits their lifestyle, learners can choose to access the program for 12 weeks or 24 weeks. To increase the course’s accessibility, Coding Temple allows anyone to enroll—no prerequisites needed.
Below we explore what Coding Temple’s Data Analytics course covers to give you a better idea of what you can expect to learn.
Introduction to Data
Since absolute beginners can take the course, training begins by introducing you to data. In the first module, you will learn the difference between various data-related roles, including those of a data analyst and a data engineer. You will also learn about the relevance of data and gain a better understanding of Coding Temple’s self-paced learning model.
Excel and Statistics
After the introductory part of the program, you will explore Excel, which is a vital component in performing data analytics. Particularly, you will learn about data entry, tabulation, formulas, and so on, which you’ll eventually apply to create projects with Excel such as an attendance tracker and a personal expense tracker.
Next, the program introduces you to beginner and intermediate statistics, covering fundamental concepts like distribution, percentiles, causation, and correlation. Inferential and descriptive statistics are also part of the module, as well as experiments and their evaluation.
Another vital part of data analysis is learning SQL. You will learn how to move data back and forth from SQL, Excel, and CSV files. You will also learn how to build, query, sort, and update relational databases with SQL. You will explore the fundamentals of relational databases in this segment and get the chance to practice SQL until you are proficient at the intermediate level.
You will spend a significant part of the course on Python. The first part of this section will focus on basic Python programming, the same curriculum used in Coding Temple’s self-paced Software Engineering course.
The second part will teach data analysis using NumPy, Pandas, Matplotlib, and SciPy. You will familiarize yourself with how to automate Excel using Python. To develop mastery, the Python sections will require you to create small projects utilizing this programming language, such as interactive games and shopping carts.
With the help of this module, you will be capable of writing scripts in Python, PostgreSQL, and the shell. You will learn how to execute scripts and basic Linux commands. The Data Analytics course will also teach you how to write a few scripts in Coding Temple’s command line interfaces.
This module will teach you about writing non-relational data and how to use MongoDB’s Compass software. Moreover, you will become proficient in using Python to get data from MongoDB and JSON documents hosted online. This part of the program will also include an introduction to cloud data.
R and Other Data Tools
R is a programming language created explicitly for use in statistics. You will learn to use R and other tools such as RStudio, ggplot2, and tidyverse, among others. This section will also discuss exploratory data analysis.
The program will then focus on business software essential to data analysts, such as Tableau and Power BI. Expect to learn more about data ethics, particularly how to generate good questions, perform storytelling in statistics, and become aware of the common pitfalls to avoid in data analysis.
The program caps with two projects. The first project is a Parallel Analysis Project, where you will analyze a data set using Python, SQL, Excel, and R. This project aims to sharpen your understanding of the differences between various tools. You will form a hypothesis, explore data sets, and produce a final written report detailing the exploration process.
The second project is an Analysis Presentation Project. Here, you will choose a data set and suitable tools like Python, SQL, Excel, or R and explore the data provided using your preferred language. The final product is a five to 10-minute recorded presentation explaining your results.
Learning and Career Support
Coding Temple’s Data Analytics course offers all the features the bootcamp is well known for, including one-on-one tutoring and mentoring and 24/7 community access. You can also engage with other individuals attending the same program or those who have already completed the program. You will also have access to weekly group sessions and even get the chance to participate in live instruction for extra practice.
Career support is a crucial part of the Coding Temple experience. You will go through mock and behavioral interviews and one-on-one mentoring and coaching sessions. The bootcamp’s extensive career support system has helped Coding Temple alumni to work in top companies such as Amazon, Accenture, and LinkedIn.
Apply to Coding Temple Today
Data analytics is an in-demand field, and the role of a data analyst is now essential in many companies and organizations. Coding Temple’s Data Analytics course equips its students with a solid foundation and the necessary skills to start a career in this field.
Want to get started? Schedule a call to learn more about Coding Temple’s self-paced Data Analytics course.
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