DataCamp Course Certificates

• Data Analyst with Python - In this track, I learned how to import, clean, manipulate, and visualize data. Through interactive exercises, I got hands-on with some of the most popular Python libraries, including pandas, NumPy, Matplotlib, and many more. I gained experienced of working with real-world datasets, including data from the Titanic and from Twitter's streaming API, to grow my data manipulation and exploratory data analysis skills, before moving on to learn SQL skills necessary to query data from databases and join tables.

• Data Analyst with R - In this track, I learned how to import, clean, manipulate, and visualize data in R. Through interactive exercises, I got hands-on with some of the most popular R packages, including ggplot2 and tidyverse packages like dplyr and readr. I also developed my data manipulation and exploratory data analysis skills by working with a wide range of real-world datasets, including everything from Australian population figures to Netflix films. I then gain the SQL skills necessary to query data from databases and join tables.

• Data Engineer with Python - In this track, I discovered the foundations I needed to know to become a data engineer by learning Python, SQL, and Git from scratch. I discovered how to interact with relational databases to query, input, and modify data and get hands-on experience in importing and cleaning data in Python, optimizing my code for efficiency, and writing tests to validate my code. Additionally, I learned topics such as cloud computing, cleaning data, and working with Git. I learned key concepts and skills required by data engineers such as how to interpret data visualizations, create functions, and utilize version control.

• Data Scientist with R - In this track, I learned how this versatile language allows one to import, clean, manipulate, and visualize data. Through interactive exercises, I got hands-on with some of the most popular R packages, including ggplot2 and tidyverse packages like dplyr and readr. I then worked with real-world datasets to learn the statistical and machine learning techniques I need to write my own functions and perform cluster analysis.

• Machine Learning Scientist with R - In this track, I mastered the essential skills of a machine learning scientist using R. I augmented my R programming skill set with the toolbox to perform supervises and unpervised learning. I learned how to process data for modeling, train my models, visualize my models, and assess their performance, and tune their parameters for better performance. In the process, I got an introduction to Bayesian statistics, natural language processing, and Spark.

• Quantitative Analyst with R - In this track, I learned how to use R programming for finance where topics including portfolio analysis, time-series analysis, and forecasting were covered.

• Statistician with R - In this track, I mastered the essential skills of a statistician using R. I learned how to use statistical methods to explore and model data, draw conclusions from a wide variety of datasets, and interpret and reporting findings.

• SQL Server Developer - In this track, I learned the basics of database design, and the different SQL data types. I learned how to write queries, functions, and stored procedures. I also found out how to manage transactions, handler errors, and improve query performance.