My Datacamp Course Journal in 2022
This blog includes my short journal after completing each course in order to keep track of my progress. How far can I go in 2022!?
[12 Jan 2022] Course 1/2022 — Data Manipulation with pandas
1st month, 1st completed course.
Here we gooooo.
After struggling with pandas, googling the solutions from StackOverflow from time to time, remembered the syntax here and there, on and off. I believe it’s high time for me to start from the top. instill a strong foundation of this library.
well then…. how about me becoming Pandas goddess by the end of this year? 😎😎😎😎
[21 Jan 2022] Course 2/2022 — Joining Data with pandas
Learned [melt, merge,merge_ordered,merge_asif] functions
some functions use `pd.function` e.g. pd.concat
some use df.function e.g. df.melt, df.merge
When the naming convention is different, it kinda baffle me a bit 😵😵😵 gotta review a lot in order to remember them to its core.
On to the next one!
[9 Mar 2022] Course 3/2022 — Cleaning Data in Python
After 2 course in January. I skipped February due to my tight schedule and a bit of laziness lol… This course looked easy at first. Yet, the last chapter on cleaning text, compare the similarity between 2 values using fuzzywuzzy or perform a record linkage were entirely new topics to me.
[24 Mar 2022] Course 4/2022 — Reshaping Data with pandas
One of a ride course. For those who use a lot of data transformation are going to like this course. So many functions out here which I haven’t tried of heard about them before.
The difference between pivot, pivot_table, swaplevel
json_normalize ← a good one!
[8 Apr 2022] Course 5-6/2022 — AI Fundamentals & Machine Learning for Business
2 courses in a row! business-inclined courses which were easy to grasp for a general overview purpose.
AI Fundamentals walked us through since the AI terms, what’s its fuzz about. One topic I did love from this course was when they provided algorithms and its succint pros&cons e.g. Popular clustering algorithms are KMeans(support only linear, need to specify number of cluster ourselves), Spectral (support non linear, need to specify number of cluster ourselves), DBSCAN (support non linear, the algorithm specifies number of cluster by itself).
Machine Learning for Business a continuation course from AI Fundamentals. Explained the flow of how ML implements. What are the common risks, issues that the company faces? with the example of business requirements.
[22 Apr 2022] Course 7/2022: Data-Driven Decision Making for Business
This one surely is TOUGH! It is the 1st time that I learned DataCamp course that has numerous business cases demonstrated. For instance, propensity model, BASS model, CAPM chart, TAM-SAM-SOM. so many new topics to explore! 🤯🤯🤯🤯
[22 Jun 2022] Course 8/2022: Statistical Thinking in Python(Part 1)
It’s been 2 months since I last finished the course! This course has 2 parts, and this is just the first one. It explored statistics concept such as CDF—Cumulative Density Function, PDF—Probability Density Function which were unearthing to me. (Well, I’m not STAT kid). Mostly of the course involved with visualization plotting. To be honest, probability concept was a tough one in a probability concept since these topics need to take an additional time to read further, and DC course just provided us the overview of the concept. Let’s see how part 2 goes!
[30 Aug 2022] Course 9/2022: Statistical Thinking in Python(Part 2)
It is highly advisable to take the introduction of Statistics before taking this course because it used the prerequisite knowledge from prior courses e.g. bootstrap If you’re essentially new of forgot a long of STAT knowledge, it’ll be confusing and you need to rewatch the course.
[23 Nov 2022] Course 10/2022: Introduction to Linear Modeling in Python
Wow it’s been 5 months since I last finished the course!
This course walked you through the basic of Linear Modeling. What’s Linear. How to visualize slope&intercept. The concept of Residual Sum of Square(RSS), R-Square, Root Mean Square Error(RMSE) It is recommended to study statistics before starting this course, but my studying order is wrong! 😂
It’d better repeat this course once again.
[26 Nov 2022] Course 11/2022: Introduction to Data Science in Python
Since I have a goal to complete a current Data Analyst with Python career track, so I had to take this course even though it’s an introduction course. Yet, it’s a good course to review your fundamental and have a glimpse of each library— numpy, pandas, seaborn, scipy that you most touch in this profession. Since it’s an intro. It didn’t take long to complete.
[29 Nov 2022] Course 12/2022: Introduction to Statistics in Python
To be honest, this was better than I first expected. Taught the basic of descriptive statistics. What’s mean, median, Interquartile (IQR), each distribution types. Central limit theorem. very recommended to catch a glimpse of statistics.
[30 Nov 2022] Course 13/2022: Introduction to Data Visualization with Seaborn
This was the first time I studied using Seaborn comprehensively because I use Tableau for the most of my work. Just discovered that import seaborn as sns. sns here comes from one of the character name in TV series
[Dec 2022] Course 14-16/2022: EDA + Sampling + Hypothesis in Python
gotta summarize all three courses into one recap LOL. I took these courses consecutively as I need to complete this Data Analyst Career track. They taught you what are PMF, CDF. Type of sampling and hypothesis testing such as z-test, t-test, chi-square, ANOVA (better rewatch these courses as there are a lot of modules to be covered.)
annnndddd Here we goooo 🎉🎉🎉
Complete the track of Python Data Analyst! 36 hours in total.
Related blog posts
Most of my blogs are in Thai but there is an Google Translate option inside the blog: