Tugas Week 2 ~ Assignment

Vanessa Ziba Ardelia

Data Science 25 – ITSB

Dosen Pengampu: Bakti Siregar, M.Sc., CDS.
Mata Kuliah: Statistika Dasar

R Programming Data Science Statistics

0.1 What is Data Science Programming?

Data Science Programming is the use of programming languages like Python and R to collect, process, analyze, and visualize data. It allows us to turn raw data into meaningful insights, build predictive models, and automate decision-making. By combining programming, statistics, and domain knowledge, data science programming helps solve real-world problems in business, healthcare, finance, and technology.

1 What is the main purpose of our study (Data Science Programming)?

The main purpose of studying Data Science Programming is to learn how to extract meaningful insights from data using programming techniques.

Through programming, we can:

  • Clean and preprocess raw data
  • Perform statistical analysis
  • Build predictive models
  • Create data visualizations
  • Automate decision-making processes

In short, data science programming helps us transform raw data into valuable information that can support business, research, and technological innovation.

2 Why do we learn about it?

We learn Data Science Programming because data is everywhere in today’s digital world. Every application, website, and system generates data.

By learning it, we can:

  • Solve real-world problems using data
  • Make data-driven decisions instead of guessing
  • Develop intelligent systems (AI & Machine Learning)
  • Increase career opportunities in technology and analytics

Programming skills allow us to implement algorithms efficiently and handle large-scale data processing.

3 What tools do we need to become an expert?

To become an expert in Data Science Programming, we need to master several tools:

  1. Programming Languages:
  • Python (NumPy, Pandas, Scikit-learn)
  • R (dplyr, ggplot2, tidyverse)
  1. Data Visualization Tools:
  • Matplotlib
  • Seaborn
  • Tableau / Power BI
  1. Database Management:
  • SQL
  • MySQL / PostgreSQL
  1. Machine Learning Frameworks:
  • TensorFlow
  • PyTorch
  1. Development Tools:
  • Jupyter Notebook
  • RStudio
  • Git & GitHub

Mastering these tools allows us to efficiently analyze, model, and deploy data-driven solutions.

4 What is your interest domain knowledge in Data Science?

My interest domain in Data Science is Machine Learning and Predictive Analytics.

I am particularly interested in:

  • Building predictive models
  • Pattern recognition
  • Data-driven decision systems
  • Artificial Intelligence applications

I am also interested in applying programming skills to real-world fields such as:

  • Business analytics
  • Financial forecasting
  • Healthcare data analysis
  • Social media sentiment analysis

Programming enables me to turn theoretical statistical concepts into practical, real-world solutions.