Due to the increasing number of organizations using big data to enhance their decision-making, the need for data analytics professionals is on the rise. In the past, businesses used to depend on intuition or trial and error, but now many have realized the significance of collecting business analytics and datasets to recognize market trends and comprehend customer behavior and motivations.
Data analysts are becoming more valuable to businesses, leading to an increase in job availability. According to an IBM report, the demand for professionals in data-related positions like data scientists, business intelligence analysts, and operations analysts grew by 28% in 2020. The Bureau of Labor Statistics predicts that this field will continue to grow by 30% in the next decade. Additionally, ZipRecruiter reports that senior data analysts’ average salaries have increased, with approximately 53% earning a six-figure income.
The education industry has risen to meet this demand for data analysts, with online bootcamps developing detailed curricula to prepare prospective students for the workforce; massive open online courses (MOOCs) spinning up programs that offer certifications; and troves of free online resources promising to teach prospective students everything they need to know—from Python and data analysis skills to data visualization and how to approach analytics with spreadsheets—through free videos and blog posts.
The decision between self-taught and mentor-guided courses can be overwhelming due to the abundance of both free and paid resources. It leaves one wondering if paid classes are superior and if self-learning is a reasonable starting point. Additionally, one may question whether it is feasible to successfully become a data analytics professional through self-training.
If you’re considering a career in data analytics but are uncertain about whether to sign up for a bootcamp or pursue online courses for self-learning, this guide provides responses to some of your pressing queries.
What is the difference between a data analytics course vs. a bootcamp?
MOOCs, which are frequently used for data analytics education, generally employ a hands-off approach to teaching and may concentrate on specifics such as introducing novice learners to the fundamentals of data analytics, conducting an accelerated program in Python or SQL, or delivering a refresher on statistics and probability. Available through platforms like Coursera, Udacity, Udemy, Codecademy, DataCamp, Khan Academy, EdX, and Simplilearn, these courses enable learners to progress at their own speed, frequently necessitate self-guided instruction, and may also feature certifications while potentially receiving college credit.
On the contrary, bootcamps are brief educational programs that generally provide a more practical and comprehensive approach to learning. Rather than just teaching the fundamental concepts of a subject or concentrating on one aspect, numerous bootcamps incorporate several resources such as video lectures and tutorials, readings, practice sessions and assignments, case studies, final year projects, and some form of coaching to equip students with the skills to perform tasks such as data analysis and job interview success. An instructor is often present to resolve doubts, while mentors and counselors are accessible to give professional and academic advice. Bootcamp costs may range from $1,000-$10,000.
All bootcamps are not equal. To select a bootcamp, you should take into account the extent of the curriculum, the time involved, whether you will have the opportunity to work on real projects and making a portfolio, and the presence of career guidance and counseling. It is not enough for a high-quality bootcamp to teach you the essential abilities for functioning as a data analyst; it also needs to help you secure the job you want.
What’s covered in a data analytics course or bootcamp—and what should you expect?
Data analytics involves numerous areas, and many of them share common competencies. Before taking a data analytics course, it’s crucial to figure out if the course is broad and provides fundamental data analysis skills that are versatile and adaptable, allowing you to develop them in the future, or if it furnishes all the necessary knowledge for you to pursue a job as a data analyst.
Whether they are designed for general knowledge or career advancement, most courses offer instruction in statistical analysis, database querying, data cleansing, big data analytics, and working with data visualization tools like Tableau and PowerBi. Besides these technical competencies, many courses also provide guidance on leveraging analytical skills to solve business problems, identifying actionable business insights, building financial analysis modules, and effectively communicating findings to stakeholders.
Bootcamps that are most effective also offer chances for practical involvement in the industry or projects, as well as support from professionals in the field to guide graduates in managing their projects and finding employment.
Who is eligible for a data analytics course or bootcamp?
While certain courses in data analytics or data science may mandate previous knowledge of programming and familiarity with fundamental probability and statistics, there are several that accept individuals without such expertise, as long as they possess exceptional critical thinking and problem-solving abilities.
Ensure that you thoroughly review a course’s requirements prior to enrolling.
How long is a typical data analytics course or bootcamp?
On average, a data analytics bootcamp tailored to train job-ready graduates lasts six months, requiring a time investment of 15-20 hours per week. However, if you must fulfill prerequisites like mastering coding languages, the duration will be longer.
In a data analytics course or bootcamp, what is the first thing I should prioritize learning?
Although some data analytics courses do not require prerequisites, having some knowledge of programming languages like Python or SQL and an understanding of probability, statistics, and concepts like mean, median, and standard deviation will allow you to maximize your learning experience in a course or bootcamp.
BONUS: How to Learn Big Data?
Big data refers to the vast amount of data that is created on a daily basis by individuals, encompassing a wide range of information, including a single Facebook post.
The amount of data being generated is growing rapidly. A report predicts that by 2025, the world will produce 463 exabytes of data per day, equal to 212,765,957 DVDs every day.
Big data analytics play a crucial role in handling and analyzing large volumes of data, particularly unstructured data such as images, texts, audios, and other types.
The process of identifying valuable patterns from massive amounts of unstructured data is known as big data analytics. This concept encompasses a range of steps, beginning with data cleaning and culminating with pattern discovery. Storing and processing colossal quantities of data is the fundamental idea behind big data, which is commonly referred to as the “3 V’s.”
- Volume- It refers to the size of data, which means how much data is generated.
- Variety- It refers to the type of data, which means which type of data is generated like structured data or unstructured data.
- Velocity- It refers to the speed of data, which means at what speed data is generated.
Let’s now proceed to the comprehensive guide outlining the Big Data process.
How to Learn Big Data Step by Step?
Step 1- Learn Unix/Linux Operating System and Shell Scripting
Having good practice in shell scripting is important because numerous tools feature a command line interface that relies on both Unix commands and shell scripting commands.
Data pipelines can be constructed using Shell Scripting, which entails creating a text file that contains a series of UNIX-based operating system commands.
These resources can provide you with the knowledge of Unix/Linux Operating System and Shell Scripting.
- Linux Command Line Basics (FREE Course)
- Shell Workshop (FREE Course)
- Configuring Linux Web Servers (FREE Course)
- Linux Fundamentals (Coursera)
- Introduction to Bash Shell Scripting (Coursera Project)
Step 2- Learn Programming Language (Python/Java)
The reason why Java remains a fundamental component for various big data frameworks is due to the fact that a number of significant core modules in prevalent big data tools are programmed in Java.
Big Data processing can also be performed using Python, however Java provides a more straightforward approach which eliminates the need for third-party assistance.
The choice of learning either Java or Python is yours.
Hadoop is a framework available in Java that enables the development of big data applications, while Python boasts numerous open-source libraries and tools in abundance. For those who are new to programming, Python is a good place to start as it is relatively simple to comprehend and implement. However, if you are already experienced, Java is the way to proceed.
Let us now explore the tools available for learning Java and Python.
- The Python Tutorial (PYTHON.ORG)
- Python for Absolute Beginners! (Udemy)
- Python for Everybody (Coursera)
- Python 3 Tutorial (SOLOLEARN)
- CS DOJO (YouTube)
- Programming with Mosh (YouTube)
- Corey Schafer (YouTube)
- Python Crash Course (Book)
- Java Programming Basics (Free Course)
- Become a Java Programmer (Udacity)
- Become a Java Web Developer (Udacity)
- Core Java Specialization (Coursera)
- Introduction to Java (Coursera)
- Java Programming Masterclass covering Java 11 & Java 17 (Udemy)
Step 3- Learn SQL
SQL is the most demanding skill for Big Data . That’s why you should have a strong understanding of SQL. Knowledge of NoSQL is also required because sometimes you have to deal with unstructured data.
By experimenting with SQL in relational databases, we can gain insights into how to query vast amounts of data.
- Learn SQL Basics for Data Science Specialization– Coursera– This specialization program is dedicated to those who have no previous coding experience and want to develop SQL query fluency. In this program, you will learn SQL basics, data wrangling, SQL analysis, AB testing, distributed computing using Apache Spark, and more.
- Excel to MySQL: Analytic Techniques for Business Specialization– This Specialization program is offered by Duke University. This is one of the best SQL online course certificate programs. In this program, you’ll learn to frame business challenges as data questions. You will work with tools like Excel, Tableau, and MySQL to analyze data, create forecasts and models, design visualizations, and communicate your insights.
- W3Schools– You can learn DBMS and its concepts from the Free Tutorial of W3Schools.
- NoSQL systems– In this course, you will learn how to identify what type of NoSQL database to implement based on business requirements. You will also apply NoSQL data modeling from application-specific queries.
Step 4- Learn Big Data Tools
After becoming proficient in Python, Java, and SQL, the subsequent phase is to gain expertise in the utilization of Big Data tools. Acquiring knowledge of Hadoop and MapReduce, Apache Spark, Apache Hive, Kafka, Apache Pig, and Sqoop is imperative.
- Intro to Hadoop and MapReduce(Udacity)- This is a completely Free Course to understand the concepts of HDFS and MapReduce. In this course, you will learn what is big data, the problems big data creates, and how Apache Hadoop addresses these problems.
- Spark (Udacity)- This is another completely Free Course to learn how to use Spark to work with big data and build machine learning models at scale, including how to wrangle and model massive datasets with PySpark. PySpark is a Python library for interacting with Spark.
- Hadoop Developer In Real World (Udemy)- This course will cover all the important topics like HDFS, MapReduce, YARN, Apache Pig, Hive, Apache Sqoop, Apache Flume, Kafka, etc. The best part about this course is that this course not only gives basic knowledge of concepts but also explores concepts in deep.
- Big Data Specialization (Coursera)– In this specialization program, you will get a good understanding of what insights big data can provide via hands-on experience with the tools and systems used by big data scientists and engineers.
Step 5- Start Practicing with Real-World Projects
Congratulations on acquiring proficient skills in Big Data! Now, it’s time to channel your efforts towards Real-World projects as they are crucial for securing a job as a Big Data Engineer.
The greater the number of projects you undertake, the deeper your comprehension of Data will become, as well as enhancing your Resume with more valuable experience.
If you want to learn, you can begin with real-time streaming data from social media platforms such as Twitter that provide APIs.
If you follow these steps and acquire the necessary skills, then nothing can prevent you from entering the Big Data industry.