• Understanding what machine learning is and its types for modeling
• Understanding and being able to perform Data Exploration & Data Pre-processing
• Understanding and being able to perform modeling processes with Scikit-Learn
• Understanding and being able to perform modeling processes using algorithms in Supervised Learning
• Understanding and being able to perform modeling processes using algorithms in Unsupervised Learning
• Working on a mini project that integrates all the material and previous modules to solve a business problem.

• Creating a multi-line chart
• Creating a pie chart or multi-bar chart
• Creating a distribution graph of a data using a histogram
• Creating a customized scatterplot to display visually appealing results and provide insight to the reader
• Working on a mini project to visualize retail product data based on the knowledge and skills acquired in previous chapters

• Preparing the necessary data
• Creating a plot from the prepared data
• Customizing various components in the chart that has been created, such as figure size, title, axis labels, axis ticks, style of lines/points in the chart, grid, text, and others
• Saving the created image.

• Can apply data combination techniques between Series and Dataframe using concat, merge, and join.
• Can understand the concept and apply pivot, melt, stack, and unstack techniques on a dataframe to reformat data.
• Can understand the concept and apply groupby and aggregation techniques to reduce data volume.
• Familiar with and understand the concept of time series in Pandas
• Practice working on a simple business project using pandas"

• Understanding the Pandas library and its interaction with numpy
• Understanding and practicing how to create series and dataframes in Pandas from various data types such as lists, lists of lists, dicts, or numpy arrays
• Understanding and practicing how to read datasets from various standard formats such as CSV, TSC, Excel, Json, SQL so that they can be used as a Pandas dataframe/series and how to save them in standard dataset formats.
• Be able to understand and practicing the process of indexing, transformation and slicing on dataframes
• Be able to understand and practice how to handle missing values in a dataframe
• Practice to working on simple business projects using Pandas

• Describe the tactics, techniques and procedures used by cyber criminals
• Describe the principles of confidentiality, integrity, and availability as they relate to data states and cybersecurity countermeasures
• Describe technologies, products and procedures used to protect confidentiality, ensure integrity and provide high availability.
• Explain how cybersecurity professionals use technologies, processes and procedures to defend all components of the network.
• Explain the purpose of laws related to cybersecurity.

Learned:

• the universal concepts of computer programming (i.е. variables, flow control, data structures, algorithms, conditional execution, loops, functions, etc.)
• developer tools, developer tools and the runtime environment
• the syntax and semantics of the Python language
• the fundamentals of object-oriented programming and the way they are adopted in Python
• the means by which to resolve typical implementation problems
• the writing of Python programs using standard language infrastructure
• fundamental programming techniques, best practices, customs and vocabulary, including the most common library functions in Python 3

Learned :

• Testing
• Android Jetpack
• Android Architecture Component
• ViewModel
• Studi Kasus Proyek Android Jetpack
• Studi Kasus Implementasi Architecture Component
• Repository dan Injection
• LiveData dan Retrofit
• Idling Resources
• Studi Kasus Implementasi Repository dan LiveData
• Room
• Paging
• RawQuery

Learned:

• Melakukan Generate Grafik dengan Matplotlib
• Melakukan Pembacaan File CVS dengan pandas