How to learn the IBM Data Analyst Professional Certificate (2023)

An overview of how to successfully complete the "Original" Professional Certificate as a Data Analyst

How to learn the IBM Data Analyst Professional Certificate (1)

The big brother of Google's Professional Data Analyst Certificate, the IBM Professional Data Analyst Certificate offers the Python-based counterpart to the same formula: a self-paced online data analyst course offered by a famous name in the game. technological.

These days, being able to earn an industry-recognized certificate to enhance your resume from one of tech's heavyweights is enough to get anyone's attention. With IBM's promise to teach you the skills you need to position yourself competitively in the data analyst job (with no prior experience), this course is a no-brainer for those looking to enhance their skills and make a career change.

oneyou have to startIncluded with this professional certificate from IBM are basic computer skills, high school math skills, some comfort with numbers, and a willingness to learn.

If the past year has taught us something, and little, it's that professional happiness is more important than staying in the comfort zone. Despite the uncertainty about the futureOne in four workers is considering leaving their jobpost-pandemic, a clear sign that people are putting themselves first. What better time than now to start improving your skills to secure better job opportunities?

Swindler72,633 data analyst positions open in the United States with an average salary of $74,440 and a projected growth rate of 9% over 10 years. This affordable, self-paced, easy-to-access course is the perfect way to make big life changes without ever setting foot in the door.

WhatIBM Data Analyst Professional Certificationis a fully online, self-paced professional training course developed by IBM that prepares you to become a junior data analyst.

The program consists of 9 courses that can be completed in 11 months with less than 3 hours per week. The program is 100% online, so you can learn at your own pace.

The program is also offered in 11 languages (English, Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, Spanish, Turkish and Persian), making it accessible to people from all continents.

Courses are completed through the online learning platform Coursera.

During theIBM Data Analyst Professional Certification, learn all the skills needed to break into an entry-level data analyst position.

This starts with a gentle introduction to data analysis concepts, discovering key differences between various data-related roles, learning the soft skills needed to communicate the results of a data analysis to stakeholders, and discovering key vendors within the data ecosystem. Dice. From there, you'll learn the tools of the trade needed to clean, organize, analyze, and visualize data, including:

Course 1: Introduction to Data Analysis

This course starts with a gentle introduction to data analysis concepts, the role of a data analyst, and the tools used for everyday tasks. By the end of this course, you will understand the data ecosystem, the fundamentals of data analysis, and the soft skills needed to be an effective storyteller using data.

Course 2: Basics of Excel for Data Analysis

By the end of this course, you will have developed a basic working knowledge of using Excel spreadsheets for data analysis. This course covers the fundamentals of working with spreadsheets and their use in data analysis. From there, you'll learn the fundamentals of processing, cleaning, and analyzing data using spreadsheets.

Course 3: Data Visualization and Dashboards with Excel and Cognos

This course delves into the features and functionality of spreadsheets and gives you the ability to perform basic data analysis using Excel and IBM Cognos Analytics without having to write any code.

Course 4: Python for Data Science, AI, and Development

By the end of this course, you'll go from having zero programming experience to writing Python code in just a few hours.

Course 5: Project Python for Data Science

This project course is an opportunity for you to demonstrate and practice your new Python skills working with data by building a dashboard using Python.

Course 6: Databases and SQL for Data Science with Python

This course introduces relational database concepts and helps you learn the basics of the SQL language. You'll learn how to create a database instance in a cloud, create and run SQL queries, and access databases from Jupyter notebooks using SQL and Python.

Course 7: Data Analysis with Python

This course takes you from the Python basics you learned earlier, to preparing data for analysis, performing basic statistical analysis, creating visualizations, and predicting future trends. Topics covered include importing datasets, cleaning data, manipulating data frames, summarizing data, building machine learning regression models, and building data pipelines. You'll learn how to use popular Python libraries like Pandas, Numpy, and Scipy to accomplish these tasks.

Course 8: Data Visualization with Python

In this course, you will learn how to create effective data visualizations using popular Python libraries like Matplotlib, Seaborn and Folium.

Kurs 9: IBM Data Analyst Capstone Project

This pinnacle of the program gives you the opportunity to perform a complete data analysis from the beginning of collecting data from multiple sources to submitting your data analysis report with an executive summary.

WhatIBM Data Analytics Professional-ZertifikatAccess is through the online learning platform Coursera, which has a monthly subscription fee of $39 (USD). So, if you take the suggested 11 months to complete the course, the cost will be approximately $429 (USD).

Due to the monthly cost, it may make sense to complete the course as quickly as possible to reduce costs. However, it's important to remember that this course is already significantly cheaper than a data science training (where you can pay anywhere from $3,000 to $15,000 for the experience) or if you want to pursue a master's in data science.

Coursera also offers financial aid and scholarships to those in need.

Coursera also gives you the option to audit a course or the entire program. You will not receive a certificate and will not be able to submit assignments or receive grades for them, but you will still have access to all course materials.

Despite their accessibility, MOOCs have an averageLess than 10% completion rate🇧🇷 It is even more important that you do your due diligence early on and figure out the best way to complete this certificate.

Developing your own study plan, especially if self-study isn't really your forte, will help you get through the course and successfully retain everything you've learned.

IBM suggests you complete the course in 11 months if you spend 3 hours a week working on the course material. For the average person with off-the-job commitments and a full-time job, this timeframe is reasonable. However, this course can be completed in a much shorter time frame (1-4 months) if you have no other commitments.

With over 100 hours of course content, it's impossible to get anything out of the course just passively watching the videos and not getting your hands dirty by taking notes or completing assignments and practicing problems. That is, to make the most of the course and develop the skills that qualify you for a competitive job market, you need to work.

understand the learning process.

As mentioned above, MOOCs have an averageLess than 10% completion rate, which may be due to the lack of discipline in learning on the part of the student. In people with aattention span shorter than a goldfish, it's natural for us to start something just to be distracted by something brighter and new that's coming along. Knowing this, we must be smarter, more diligent, and more disciplined in our learning to complete online courses.

However, before creating a timeline, there are a few things you need to figure out first.

In the beginning, it's easy to say that you'll study ten hours a day and finish the course in less than 3 months. Until you try to study 10 hours a day for a week and realize how hard it is, especially when you haven't done it since college. If you follow this method, you will be part of the 90% of people who do not complete MOOCs.

It's also important to remember that your brain needs time to learn things. Those who stick to itthe 20 hour ruleUnderstand that even though it only takes 20 hours to get reasonably good at a skill, those 20 hours of study can't be done in two days and don't bring any noticeable benefit. This phenomenon has to do with neuroplasticity, also known asthe brain's ability to change and adapt based on experience🇧🇷 In short, by extending the learning process over a longer period of time,The brain can start to changeand developing the stronger connections that come with learning new skills.

When you consider all of this, it suddenly becomes much clearer how you should design your study plan to promote tangible and achievable learning benefits.

Create an actionable study plan that works for you.

To be successful in this program, you must first do a little legwork. This means creating a workable study plan that works for you and your life and will help you reach your goals for completing this course.

IBM has already proposed an 11-month study plan where you spend 3 hours a week studying. On average, if you think about it, the average person spends aboutthree and a half hoursper day In your cellphone. Suddenly spending three hours a week studying things that will benefit you in the long run doesn't seem so difficult anymore.

Choose three days a week when you know you can dedicate a full hour to studying. as long as I'm formicroaprendizagemStudying data analysis is one of those things that requires longer study periods to get into that flow state and really dig into the topics.

Once you've chosen your three days, determine which time of day works best for you. There's no use studying when you know you'll be tired, distracted, or both.

Then plan what you want to accomplish during each study session. Not only does this turn your hour-long learning session into an hour-long learning session (no time wasted trying to figure out what you'll learn that day), it puts you in charge of sticking to the schedule. 🇧🇷

Print out the schedule you created and feature it prominently in your study area to motivate you to study and also to remind you of your commitment to completing the program.

Have additional sources of information handy when faced with difficult topics.

Sometimes a teacher can't teach you what you need to know in a way that you can understand and absorb it. Every teacher has their own teaching style or their own way of explaining things that sometimes don't suit your learning style.

Instead of getting frustrated, you should have additional resources available that you can use to understand difficult topics.freeCodeCampscanal do youtube,Kaggle,academia Khan,On the road to data science, and more are free resources that can help you figure out the tough stuff.

Go through all the practice problems and homework assignments that land on your desk.

You won't get better if you don't practice.

When it comes to learning data analysis, it means reviewing and completing all the practice problems and assignments on your desk. With practice and repetition, your skills and confidence will grow. Any of the best data analysts will tell you that it's their dedicated practice that has made them better at what they do.

In short, don't practice until you get it right. Practice until you do nothing wrong.

Join a Slack workspace or Discord server where others are learning data analytics to make your journey less isolating.

Online learning can be an isolated journey, especially when you have no one to sympathize with. Community is a very important factor when it comes to becoming a data analyst and it can really help you achieve your goals.

Joining a Slack channel or Discord server is just what you need to reach others and make your learning journey easier and less isolating. Here are two great resources to help you get started:

9 Math, Python, and Data Science Discord Servers You Should Join Today
6 Data Science Slack Workspaces You Should Join

stay responsible

Creating a schedule is all well and good, but only if you really stick to it. Technical topics are best learned consistently and regularly, which means you'll need to read the books every week to see tangible results.

Taking responsibility can take many forms, whether it's telling your mom you'll complete this course in 4 months, writing a blog, live streaming your studies, or joining a study group. Whatever it is, make sure it helps you develop a disciplined study habit.

Document your learning as an additional way to show employers your growth as a data analyst.

Learning a new skill is great, but documenting your learning process is even better.

Whether it's for your own benefit or to show employers your development and training as a data analyst, documenting your journey is a great way to not only remember what you've accomplished, but reinforce what you've learned so far.

Documenting your learning process can be as simple as writing in a journal, publishing a blog post, making a YouTube video, or telling someone what you learned that day. Bonus points if you can monetize it!

The central theses:

IBM offers the Python-based alternative to Google's Data Analytics Professional Certificate: a self-paced online data analyst course taught by a historic name in technology
The IBM Data Analyst Professional Certificate includes 9 courses that can be completed in 11 months and are offered in 11 languages.
By the end of the course you will have learned Excel, IBM Cognos Analytics, Python, SQL, Pandas, Numpy, Scipy, Matplotlib, Seaborn and Folium.

While this course is not a guarantee that you'll start your career as a data analyst, it does offer a guaranteed benefit: for a fraction of the cost it would cost to complete a bootcamp or master's, you'll have the opportunity to build skills in an emerging business, learning from some of the best in the business.