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Data Science Curriculum: 4 Chapters, 25 Lessons — pelatihan.org
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Data Science Curriculum
Learn Data Science online with 25 structured lessons, code examples, and 125 quiz questions.
25 lessons
4 chapters
125 questions
Summaries and key points are free to preview. Full lessons, code examples, and quizzes require a subscription.
01
Data Science
8 lessons
01
DS HOME
Data science is the science that combines statistics, programming, and domain knowledge to generate insight from data. Its work processes include collection, cleaning, analysis,…
02
DS Introduction
The introduction of data science explains the difference between raw data and information that has been processed into meaning. Data science is present in everyday life through…
03
DS What is Data
Data can be classified based on their structural level into structured, semi-structured, and unstructured. Structured data is stored in a table, semi-structured like JSON or XML,…
04
DS Database Table
A database is a collection of related tables, while a single table holds specific data in the form of rows (records) and fields (fields). The primary key serves as a unique…
05
DS Python
Python has become a major language in data science because of its simple syntax and its highly comprehensive library ecosystem, from pandas, NumPy, to scikit-learn. Interactive…
06
DS DataFrame
DataFrame is a two-dimensional data structure of a library pandas that resembles a spreadsheet table with rows and columns. We can make it from a dictionary, read a CSV file, and…
07
DS Functions
A function is a block of code named that can be called repeatedly to perform a particular task. In Python, a function is defined by the def keyword, receives parameters as input,…
08
DS Data Preparation
Data preparation or preprocessing is the process of preparing and cleaning raw data to be ready for accurate analysis. Its main activities deal with missing values, duplicate,…
02
DS Math
3 lessons
01
DS Linear Functions
A linear function has a general form y = mx + c whose graph is a straight line. Here m is the slope that shows the change magnitude of y for every addition of one unit x, whereas…
02
DS Plotting Functions
Visualizing a linear function helps to understand the relationship between variables in a moment. The Matplotlib Library in Python provides functions such as plot() for lines and…
03
DS Slope and Intercept
In the linear model y = mx + c, slope (m) describes the direction and strength of the relationship, while intercept (c) is the value of y when x is equal to zero. The slope is…
03
DS Statistics
7 lessons
01
Stat Introduction
Descriptive statistics summarize the main characteristics of a data set through central trend measurements such as mean, median, and mode. The median is the mean of the data…
02
Stat Percentiles
The percentage indicates the position of a value relative to the entire data: the N-percent means that N percent of the data is below that value. Quartiles are the four parts of…
03
Stat Standard Deviation
Standard deviations measure how far the data values are spread from the mean. Small values mean data is accumulated near the mean, whereas large values indicate a wide…
04
Stat Variance
The variance is the average square of the deviation of each data to the mean, thus being the distribution measure underlying the standard deviation. The unit is the square of the…
05
Stat Correlation
Correlation measures the strength and direction of a linear relationship between two numerical variables, expressed in Pearson coefficients between -1 and +1. A positive value…
06
Stat Correlation Matrix
A correlation matrix is a table that displays the correlation coefficients between all the variables pairs in a single dataset, thus facilitating the scanning of the relationship…
07
Stat Correlation vs Causality
The principle of 'correlation does not imply causation' reminds us that statistical relationships between two variables do not automatically mean one causes the other. Data…
04
DS Advanced
7 lessons
01
DS Linear Regression
Linear regression models the relationship between independent variables (predictors) and dependent variables (targets) to predict numerical values, e.g. house prices based on…
02
DS Regression Table
The regression results table (e.g. OLS output from statsmodels) summarizes all the important information about the model: R-squared, coefficients of each predictor, standard…
03
DS Regression Info
The information section on the regression output gives an overview of the overall quality and compatibility of the model. Among them are the number of observations, residual…
04
DS Regression Coefficients
The regression coefficient explains the magnitude of the effect of each predictor variable on the target: one increase in the predictor unit is associated with a change in the…
05
DS Regression P-Value
The P-value on regression results measures the probability of obtaining an extreme coefficient of attenuation if the null hypothesis (coefficient = 0) is correct. The common…
06
DS Regression R-Squared
The R-squared (determination coefficient) measures the proportion of the variability of the target variable successfully described by the model, with a value range between 0 and…
07
DS Linear Regression Case
Linear regression case studies apply the whole concept to real data, for example predicting exam scores based on study hours. Before modeling, the data was understood and…