Data Science with Python for Beginners

Data science is an interdisciplinary field that uses algorithms, procedures, and processes, statistics, and machine learning to analyze large data sets, uncover patterns, and guide decision making.

Discover data representation using tables and graphs, focusing on structured data, variables as columns, and observations as rows, with first-row labels indicating variable names.

Explore the pandas library in Python to analyze, clean, and manipulate data, check the relationship and the maximum value, plot graphs, and read CSV files for practical data science workflows.

Explore the numpy data science library in Python, learn to create and manipulate one-, two-, and three-dimensional arrays, perform sorting and basic linear algebra operations.

Learn how to use matplotlib to visualize data in Python, including plotting simple graphs, scatter plots, bars, histograms, and pie charts with labels and grids.

Explore SciPy, the scientific Python library, and access built-in constants like pi. Learn to use CSR and CSC matrices for sparse data and efficient data analysis.

Explore Seaborn, the Python data science library built on matplotlib, to visualize data with color palettes, load Iris and Titanic datasets, and create line plots with a dark grid style.

Explore core data science functions such as min, max, and mean, and apply them to arrays and data frames to summarize datasets.

Clean and prepare data by importing a csv into a data frame, removing nan values, and inspecting data types. Use describe to summarize math, reading, and writing scores.

Explore linear functions that relate an independent variable to a dependent variable using y = mx + c, highlighting slope and intercept in data interpretation.

Explore linear function using a variable for prediction, with y = mx + c. Relate temperature to plant growth, and explain the slope and intercept while planning a graph.

Model linear functions with one predictor to predict plant growth using y = mx + c, where temperature is the input, slope is growth rate, and intercept sets baseline.

Explore the gradient and intercept by calculating slope from coordinate pairs and using NumPy polyfit to obtain the gradient and intercept for predicting plant growth data.

Explore how statistics support data science by using descriptive statistics and the describe function to summarize data and build reliable prediction models.

Explore descriptive statistics, including count, mean, standard deviation, and percentages, to analyze 1000 students' math, reading, writing scores across eight variables. Use numpy percentile to reveal the 5th percentile.

Explore standard deviation and variance in data science, describing how observations spread around the mean and signal uncertainty. Use NumPy to compute std for a data frame.

Compute variance with numpy on numerical columns, filtering to math, reading, and writing scores, and relate it to standard deviation to understand data spread in a data frame.

Explore correlation by examining how reading score (independent variable) relates to math score (dependent variable) using a scatter plot to reveal a positive relationship and 1000 student observations.

Explore how linear regression models the relationship between variables and uses a regression line to predict outcomes, focusing on independent and dependent variables and correlation.

Apply linear regression to confirm the relationship between reading score and math score, and prepare data with pandas, numpy, matplotlib, and scikit-learn, including a train-test split.

Call and fit a linear regression model with scikit-learn, then plot the data and the regression line to show the relationship between reading score and math score.

Explore the linear regression table and its visualization using ordinary least squares, with syntax for stats model formula, fitting math_score and reading_score against a dataset.

Explore the linear regression table, with math as the dependent variable and reading score as the independent variable, 1,000 observations, using ordinary least squares and adjusted r squared.