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Exploring IBM Data Analytics: A Comprehensive Review

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Data Analytics is a skill that is becoming a need in every profession. A skill so common that you'll find multiple courses on multiple websites guaranteeing you success.

Choosing the best fit from such a huge lot is not an easy task. It needs thorough research about the course, the instructors and the platform. One such course is IBM Data Analytics with Excel and R on Coursera.




 This professional certificate is a 9 course series including 

1.     Introduction to Data Analytics 

2.     Excel basics for Data Analysis

3.     Data visualization and Dashboards with Excel and Cognos

4.     Assessment for Data Analysis and Visualization Foundation

5.     Introduction to R programming for Data science

6.     SQL for Data Science with R

7.     Data analysis with R

8.     Data visualization with R 

9.     Data science with R- Capstone Project

 

I recently finished the first course i.e. Introduction to Data Analytics. It is a self- paced course that will require around 10 hours and 27 minutes to complete but you can do it according to your time schedule. It is a 5-week course.

FIRST WEEK

In the first week, it talked about the basic Introduction of Data Analysis, responsibilities of a Data analyst and how does a day in the life of a Data analyst looks like. It also explained the qualities needed to be a Data analyst and what are the real life applications of this skill.

They provided the viewpoints of the industry experts who are actually working in the field of Data Analytics. It strengthens the credibility of the information the course is offering because we are more likely to believe someone who has firsthand experience.



SECOND WEEK

The data ecosystem and the fundamental terms used in data analytics were covered in the second week of the course. A detailed video on the types of data and the different types of data files and formats was also there. Data Sources and roles of APIs were discussed in detail with help of real life examples.

How Twitter and Facebook APIs are used to source data from tweets and posts for performing various tasks such as opinion mining or sentiment analysis and how the Stock Market APIs are used for pulling data such as share and commodity prices earnings per share and historical prices for trading and analysis.

The languages that are crucial for a data professional were covered in detail, along with an overview of data repositories and how they operate. 


THIRD WEEK

Until third week we were acquainted with the vocabulary and concepts of understanding the problem and the desired outcome. We discussed how to determine which data we'll need to address our issue.

Types of data: Primary, secondary and third party data depending on whether we are obtaining it from the original source, from external data sources or purchasing it from the data aggregators.

In this stage we have to consider the quality, security and privacy of the practices we are employing for gathering the data.

Steps to transform the raw data including joins, normalization, denormalization, cleaning and enriching were explained.


FOURTH WEEK

Role of statistics is crucial in data analysis and this course did it’s best to acquaint us with the basics of it. How statistical methods can be used in order to develop an understanding of the data were explained i.e. descriptive and inferential analysis.

Data mining and its techniques and software and tools were also discussed such as R-programming, Python and IBM SPSS Statistics etc.

Communicating our findings is very important when we are working with data. One should ensure that the audience is able to understand you and establish the credibility of your findings.

Therefore presenting your data with a structured narrative is important and hence, Data Visualization comes into play.


FIFTH WEEK

Fifth week was all about finding opportunities in this field and how one can get into data analytics.

Various viewpoints of women were given who have excelled in this particular area, which was empowering for me.

One final assignment was there in this week where we had to use data analysis skills on a real life dataset. 


CONCLUSION

Every week had a set of two graded assignments which helped me a lot in learning and applying the information we learned in the video lessons. The quality of assignments was also good. 

This was the basic one in the list of total 9 courses. Its aim was to make us acquainted with the terminologies used in data analytics. Not every topic was discussed in detail in this course since it is the first, basic introductory course.

Hence, it is a good course if you want the basic knowledge of the data analytics and if you are a beginner, it is a good start. 

Deepanshi

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