Data 101: Jobs You Should Checkout and Courses You Should Master
Nowadays, everything we do is related to data. Card data are used for making payments, human data are used for marketing targets, products sold data are used for showing hot products, and so on. There has been significant growth in the use of data, from using data for analyzing trend patterns until predictions. However, there are only a few people who are interested in going into data-related fields. Therefore, we will provide YOU about what basic knowledge you need to learn to enter data fields, data-related jobs, and the job descriptions, and what courses are provided in the Computer Science Faculty University of Indonesia to enter data fields. Hope you enjoy ^^~
What are the needs of getting into data fields?
The world of data is so big and is developing every day. But what you have to prepare for getting inside this world? First, you have to understand the business that will use your data. Because of the goals of every job in data are for making a right inside for the business. Then, you have to ready with heavy mathematics. You have to search for the relationship between one data to another with statistics and make the visualization of that daily. Next, you need to have good programming knowledge that will help you gather the data that will be analyzed. It can be basic programming to machine learning that will help you to get what you need.
What kind of data-related job is there in the world?
There are a lot of jobs that are related to data. In this story, we will categorize them into 3 jobs that we think are the most in-demand jobs in the world:
- Data Analyst
Data Analysts are those who analyze data mostly from Adobe & Google Analytics with scripting & statistical skills. They also represent data via reports and visualization to give business advice. In some places, Data Analyst is represented as Product/Marketing/Risk Analyst.
- Data Engineer
Data Engineers are those who prepare the data that will be used by Data Scientist. They ensure the accuracy and flexibility of data, develop, test & maintain the data architectures. Strong technical background with the ability to create and integrate APIs is needed.
- Data Scientist
Data Scientists are those who analyze and interpret complex digital data (Big Data). Using machine learning & deep learning, they represent the data to help the business-related decision. Mostly used for filling the gap between the stakeholders and the customers.
What courses are related to data and available to be taken in the Computer Science Faculty University of Indonesia?
- Statistics and Probability (Mandatory Course for both Computer Science and Information System) [opens on BOTH semester]
Prerequisites: Basic Mathematics, Discrete Mathematics
What you will learn: Descriptive Statistics (mean, median, mode, z-score, standard deviation, percentile, and more), Sampling Technique (Simple, Systematic, Stratified, Random, Convenience, and more), Probabilities (conditional, Bayes, independent, and more), Random Variables (Discrete and Continuous Random Variables, Variance and Covariance, and more), Distribution of Sampling Statistics, Parameter Estimation, Hypothesis Test, etc.
- Databases (Mandatory Course for both Computer Science and Information System) [opens on BOTH semester]
Prerequisites: Programming Foundation
What you will learn: Relational Model Concept and Constraints in Relational Databases (Entity, Constraint, ER, EER, and more), SQL Language (insert, delete, update, basic query, advance query, triggers, and more), Database Systems, etc.
- Intelligent System (Mandatory Course for Computer Science) [opens on BOTH semester]
Prerequisites: Discrete Mathematics, Data Structures, and Algorithms, Statistics and Probability
What you will learn: Artificial Intelligence (agents, multi-agents, philosophical and ethical aspects), Search Algorithms (informed, uninformed, non-classical, heuristics), Probabilistic Approaches, Logical Approaches, etc.
- Data Science and Analytics (Mandatory Course for Computer Science) [opens on EVEN semester]
Prerequisites: Databases, Statistics, and Probability
What you will learn: Types of Data, Linear Algebra, Probabilities, Statistics, Data Visualization, Machine Learning (Regression, Grid Search, Neural Networks, Naive Bayes, Decision Trees, KNN, k-means, Random Forest, Support Vector Machine, Gradient Boosting), etc.
- Applied Statistics (Mandatory Course for Information System) [opens on EVEN semester]
Prerequisites: Statistics and Probability
What you will learn: Descriptive Statistics (frequency distribution, variability, central tendency), Inferential Statistics (z-scores, hypothesis testing), t-statistics (independent or related samples), ANOVA, ANCOVA, regression (simple, multiple, logistic), Multivariate Analysis, Exploratory Factor Analysis, Structural Equation Modelling, Confirmatory Factor Analysis, the Chi-Square Statistics, etc.
- Advanced Databases (Specialization Course for both Computer Science and Information System) [opens on ODD semester]
Prerequisites: Databases
What will you learn: Storage and File Structures, Indexing, Query Processing, Query Optimization, Monitoring and Tuning, Transaction, Concurrency, Data Warehouse, Data Analysis and Mining, Distributed Databases, Big Data, etc.
- Machine Learning (Specialization Course for Computer Science) [opens on EVEN semester]
Prerequisites: Intelligent System
What you will learn: Perceptrons, Gaussian Discriminant Analysis, Naive Bayes, Support Vector Machine, Tradeoff, Feature Selections, Trees, Neural Networks, K-means, Factor Analysis, Iterations, Searches, etc.
- Data Mining (Specialization Course for Computer Science) [opens on ODD semester]
Prerequisites: Databases, Intelligent System
What you will learn: Attributes, Dimensional Reductions, Kernels, Linear Discriminant Analysis, Probabilistic Classifier, Decision Trees, Support Vector Machine, Neural Network, Ensemble Learning, Regression, K-means, Clustering, Graph Mining, etc.
- Image Processing (Specialization Course for Computer Science) [opens on ODD semester]
Prerequisites: Calculus, Data Structures, and Algorithms, Linear Algebra
What you will learn: Image Transformation, Image Improvement, Restoration, Colour Transformation, etc.
- Information Retrieval (Specialization Course for Computer Science) [opens on ODD semester]
Prerequisites: Data Structures and Algorithms
What you will learn: Text Processing, Information Retrieval, Sentiment Analysis, etc.
- Topics in Architecture & Infrastructure (Specialization Course for Computer Science) [opens on ODD semester]
Prerequisites: -
What you will learn: Cloud Computing, Container, Docker, Serverless Computing, Automation, and Auto Scaling, Microservices, etc.
- Natural Language Processing (Specialization Course for Computer Science) [opens on EVEN semester]
Prerequisites: Databases, Statistics, and Probability
What you will learn: Sentiment Analysis, Information Extraction, Information Retrieval, Searches, etc.
- Big Data Management (Specialization Course for Information System) [opens on ODD semester]
Prerequisites: Databases
What you will learn: Data Mining Techniques, Non-Relational Data Models, Distributed File Systems, Map Reduce, Big Data Analytics, NoSQL, etc.
- Data Mining and Business Intelligence (Specialization Course for Information System) [opens on BOTH semester]
Prerequisites: Databases
What you will learn: Decision Making, System, Modelling & Support, DSS, Data Mining for Business Intelligence, Neural Network for Business Intelligence, Text and Web Mining, Artificial Intelligence, Business Performance Management, Knowledge Management, Association Analysis, etc.
- Social Media Analytics (Specialization Course for Information System) [opens on EVEN semester]
Prerequisites: Data Structures and Algorithms, Statistics & Probability
What you will learn: Sentiment Analysis, Information Extraction, Information Retrieval, Searches, etc.
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Source:
- https://www.edureka.co/blog/data-analyst-vs-data-engineer-vs-data-scientist/
- https://medium.com/@vegi/data-scientist-vs-data-analyst-vs-data-engineer-using-word-cloud-902ab83d0879
- BRP of each and every course
Special thanks to Nurdela Ardiansyah for helping me making this article (:
Feel free to contact me anytime (questions, critiques, etc) to kellywillliam@gmail.com
Thank you!
