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(Paid 4 Sessions) Master The Skills of Data Scientist

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Dattabot Dojon

Graha Tirtadi Ground Floor

Jalan Senopati No.75, Kebayoran Baru RT.8/RW.3

Selong, Kebayoran Baru, South Jakarta, Daerah Khusus Ibukota Jakarta 12190

Indonesia

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Registration on Eventbrite isn't counted. For registration and more details about this 6 sessions of Digital Mastership, head up to http://lingkaran.co/cs-events/data-scientist/




In a Data-driven economy, every industry and sector now looking to data and analytics to drive competitive differentiation. With data science, companies able to explore a variety of information, from consumer behavior, production process, profit prediction and extract other valuable data for better decision making. That’s why the demand for Data Scientist is going up steadily and referred to as “the sexiest job of the 21st century”.

If you passionate about Data Science and looking to move across into the Data Science field, you need to have a solid interest in data landscape and key skills of statistical data analysis, how to analyzing data, and using your insights to solve wider business problems.

This course is designed to prepare you to jump into data Data Science field. Our mentor will guide you to understand the foundations of Data Science, how to transform and clean raw data, create and interpret descriptive statistics, and mastering data visualizations.


What You'll Learn:

1. Intro to Data Science & Basic Data Processing in R

First Hours:

- What data science is and why it becomes so popular
- The jobs related to data science (data scientist, data engineer, data analyst, data engineer, business analyst, machine learning engineer, etc.)
- Focus to data scientist and introduce deeper to the role
- The tools usually used and the usages

Second Hours:

- Introduction to R and RStudio (what can be done using R, how to install, the workspace, the features, the limitations)
- Set the working directory
- Simple calculations and syntaxes
- Data classes of R (matrix, vector, list, factor)
- Access internal data R provides and explore it (subset, change column names, etc)


2. Importing and Processing Data

- Introduction to packages
- What data types (extensions) can be processed in R
- Import and export data (xlsx, txt, csv)
- Some data exploration and cleansing (filling NAs, rescale, etc) guided by study cases
- Conditionals, looping
- Aggregate and Merging
- Using plyr and dplyr
- Connect to database (optional, maybe just to show as an example)


3. Data Visualization & Analyze Data Distributions

First Hours:

- Simple plot
- Boxplot, bar chart
- Syntax and logics (grammar) of ggplot2
- Plot using ggplot2 (include facet)

Second Hours:

- Show statistical summary (mean, mode, median, percentiles)
- Binning
- Density plot and histogram
- Correlations
- Simple linear regression and how to interpret
- Others kind of regression (exponential, logarithmic) → GLM


4. Introduction to Shiny

- What is Shiny and what are the advantages compared to usual visualization
- Structures of Shiny (UI and Server)
- Logic and Syntax
- Create simple interactive visualization

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Location

Dattabot Dojon

Graha Tirtadi Ground Floor

Jalan Senopati No.75, Kebayoran Baru RT.8/RW.3

Selong, Kebayoran Baru, South Jakarta, Daerah Khusus Ibukota Jakarta 12190

Indonesia

View Map

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