One of the most sought professional careers in this technological age is a Data Analyst.
The high demand for this profession makes it a minefield, this means high remuneration, great job security, and an abundant career path.
Therefore, being a skilled professional data analyst puts you at the top of the food chain. It is very possible to learn this professional skill on your own.
Having basic knowledge in computer science is a significant advantage and foundation, but you can also learn from scratch with a data analysis crash course.
It is important to note that data analysis inclines towards continuous learning and staying updated with the latest skill, tool, language and trends.
Data analysis crash courses over the years have proven to be extremely effective.
With vigorous research and constant learning, you can achieve your goal and also equipping yourself with the right hard skills and soft skills of data analysis gives you a head’s way in the data analysis world.
Skills Needed in Data Analysis
Data analysis involves collection and analysis of large data, specifically understanding, analysing, communicating, building and managing data and its database.
Certain skills are very important in understanding the basics of data analysis. These skills could be hard skills or soft skills.
They act as a foundation to build your professional career and make your learning easier when you take the data analysis crash course.
Soft skills proven to be of great help in data analysis are;
As the demand for data analysts skyrocket, it is important for you to possess certain hard skills that would make you thrive and also land a top position in the Data analysis field.
- Microsoft Excel: Over the years, Excel has remained an important tool for businesses in every industry.
Although there are a few instances where advanced Excel methods such as Macros and VBA lookups are still used for smaller tasks, lifts and quick analytics, this also significantly depends on the complexity of the task.
- Structured Query Language (SQL): Data analysts need to know how to use the industry-standard database language, Structured Query Language (SQL).
The language is usually considered as a high-level version of Excel. It is able to deal with data that is too big for Excel.
- NoSQL: NoSQL databases are precisely preferred for their fillip aspects of easy horizontal scalability and flexibility in a given organisation.
Similarly, they are being chosen cause of quick turnaround for in-process analysis and the accessibility over consistency of data.
- Critical Thinking: To be successful in the analyst’s role, you need to act as an analyst.
It is a responsibility of a data analyst to discover and make sense of correlations that are not always that obvious.
Data analytics involves asking the right questions by identifying what to ask.
- R: Data wrangling is the act of preparing data for business intelligence tools.
With its user-friendly interface, R has many add-on packages that are data-centric and which users can use to visualize data and also perform advanced statistical functions.
R is data analytical and scientific, while Python is more like a general-purpose language.
- Python - Statistical Programming: Python is one of the most important programming languages for data analysts.
Data analysts use it to direct the power of AI and ML on extensive data sets.
Python comes with many specialized, open source libraries, many of which pertain to machine learning/augmentation.
- Data Visualization: Data visualization can have a make-or-break effect on the impact of your data.
Analysts use eye-catching, high-quality charts and graphs to present their findings. Tableau’s visualization software is considered an industry-standard analytics tool.
- Statistics: Most data analyst job postings do not require a mastery of statistics. Basic knowledge of some statistical concepts is important to understand.
These are the concepts that form the foundation of machine learning, which powers predictive data analytics. Some basic concepts to understand are significance testing and linear and logistic regression.
- Machine learning: An understanding of machine learning has been identified as a key component of an analyst’s toolkit.
You’ll need to have your statistical programming skills down first to advance in this area. An “out-of-the-box” tool like Orange can also help build machine learning models.
9 Best Data Analysis Crash Course To Take Your Career To The Next Level
The emergence of online courses has paved a way for formal education.
Here, professional courses are easily accessible from the comfort of your home or office, even in the wee hours of the morning.
Here are the 9 best data analysis crash courses to advance your knowledge and skills;
Learning Python for Data Analysis and Visualization: This online learning platform is fast growing in the online field.
Udemy offers a bulk hold of professional courses for all kinds of career life as a token.
You can also access it with a discount, and they even offer free classes. It offers various data analysis crash courses that can help advance your career.
According to reviews by students, Learning Python for Data analysis and visualization (2019) is one of the best data analysis crash courses Udemy offers.
As a growing data analyst, the course critically upskill you to an intermediate level of python programming, which we already confirmed as one of the hard skills one must have to be a professional data analyst.
This course also explores how to work with formats within python like HTML, MS excel Worksheets and even helps students create a portfolio of various data analysis projects.
Statistics for Data Analysis Programme: The Statistics for Data Analysis programme is one of the best programmes for data analysts.
It includes describing data, understanding probability theory, designing experiments, interpreting statistical results, and applying statistical models with Python.
This is a great approach to visualizing large data.
Udacity is an online platform that offers an online programme, which prepares your career as a data analyst and helps you gain required skills both in theory and practicals.
It can run just for 3 months, and it qualifies you to work as a data analyst.
IBM Data Analyst Professional Certificate: This programme unlocks the potential of every data analyst seeking to become a professional.
It describes the data ecosystem and composes queries to access data in the database. Coursera, a notable online learning platform, offers this programme.
Throughout this learning, Coursera promises hands-on projects and labs, gaining a firm grip on technical skills and an opportunity to complete a real-world capstone project.
Applied Data Science with Python Specialization: offered by the prestigious University of Michigan on Coursera platform.
This course helps you gain insight into data and its database, it helps you gain the important skills applicable to data science method, techniques and machine learning.
It takes approximately 5 months to complete, has a flexible schedule and gives a certificate upon completion.
Executive Data Science Specialization: While understanding the different aspects of data science, it is important that one understand how to lead a data analysis team.
This course equips you with leadership, communication, data management skills, and machine learning.
It allows you to ask the right questions, learn to assemble, recruit and evaluate your team as a team lead.
This course gives you a sense of confidence in your field as you learn the concept behind data science.
Introduction to Data Science: This course by Simplilearn is a course for beginners ready to dive into the data world.
It explores the basic data algorithm, the top data jobs, and skills.
In this data analysis crash course, you’ll learn inferential statistics, descriptive statistics, and natural language processing.
It interestingly comes with a certificate of completion. If you are looking into just starting your career in data analysis, this should be your first click.
Data Science Fundamentals: The Corporate Finance Institute (CFI) compounds this exceptional online course. It critically analyzes the cycle of data science and the fundamentals of machine learning.
This course is carefully crafted, heavily descriptive and highly educational. If you are an aspiring data analyst, this course will build you up with speed into a professional.
With its prep courses, core courses and elective courses, it also issues a certificate at the finish.
The United Kingdom CPD certification service certifies data analysis for management.
The course module is divided into 8. On completion you walk away with a capstone project, visualization and reporting skills, and then a certificate validating your data analysis skills and knowledge.
Analyzing Data with Excel: Excel is an important skill a data analyst must possess.
This is an introductory course that builds your fundamental knowledge required in using spreadsheets to perform basic data analysis.
It’s 5 weeks of rigorous learning, self-paced and free.
Conclusion
As an aspiring data analyst, self-growth and passion are very important. I’m not exactly guaranteeing a smooth sail.
Of course, it might be a bit technically challenging, but with great dedication and hard work, I can guarantee success.
And again NO!, you don’t need a degree in computer science to be a data analyst or to take data analysis crash courses.
