Preparation for Data Science Track - Online Learning with Certificate

Preparation for Data Science

Intermediate

4.6

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Track curriculum encompasses a collection of pivotal courses that provide foundational knowledge and skills essential for a successful journey in the …

219

Chapters

Learning hours

185

Assignments

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What is Data Science?

Numpy in a Nutshell

Pandas First Steps. Advanced Techniques in Pandas

Probability Theory Basics

Learning Statistics with Python

Probability Theory Mastring

In this section we will get acquainted with what the NumPy library is, as well as learn how to create an array.

What is NumPy?

Function array()

Basic Array Creation Quiz

In this section we will get acquainted with arrays of different dimensions, and understand the difference between them.

1-D Arrays

Mathematical Operations with 1-D Arrays

2-D Arrays

3-D Arrays

Multidimensional Arrays Quiz

In this section we should recall what slices are and learn how to make them for arrays of different dimensions. We shall also learn to refer to elements in an array using their indexes.

Access Array Elements

Match Math Operations

Access 2-D and 3-D Arrays

Match the Slicing

Negative Indexing

Match the Indices

Get One Dimensional Array Using Slice and Only Positive Indexes

Get One Dimensional Array Using Only Negative Indexes

In this section we will learn how to reshape arrays and also how to concatenate an array. Moreover we will learn how to sort an array. Also we will learn about such an interesting method that is often used for arrays, namely: copy().

Creating NumPy Arrays

Manipulating and Indexing

Basic Statistical Operations

Linear Algebra Operations

Reshaping and Stacking

Saving and Loading Arrays to/from Files

Dive into the fundamentals of the pandas library. Learn to create and manipulate Series and DataFrames, explore their structure, and build confidence in performing essential data operations.

What Is pandas?

Series

Challenge: Creating a Series

DataFrame

Quiz: Creating a Series

Quiz: Creating a DataFrame

Adding a New Column

Inserting a New Column

Deleting a Row/Column

Quiz: Matching the Functions

Working with Columns

Quiz: Extracting Columns

iloc[] Basics

Challenge: Using iloc[]

Master how to load and manage data from CSV and TXT files. Gain practical experience importing datasets and preparing them for further analysis using pandas tools.

CSV Files

TXT Files

Quiz: Working with Files

Quiz: Accessing the Columns

Learn to explore, clean, and summarize data effectively. Understand how to handle missing values, extract insights, and perform basic statistical operations with pandas.

Viewing the Data

Quiz: Using head()

Quiz: head(), tail(), and sample()

Exploring the Dataset

Column Names and Data Types

Finding Null Values

Quiz: Identifying Null Values

Challenge: Dropping Null Values

Challenge: Filling Null Values

Quiz: Null Values

Describing the Data

max() and min()

Quiz: Statistical Operations

sum() and count()

Unique Values

This section will teach you how to output specific columns by their titles or indices. Also, you will get acquainted with the ways you can select rows by indices.

Outputting Columns by Title

Outputting Rows by Index

Selecting Specific Rows and Columns

Learning More About Indexation

Getting Familiar With lambda Functions

Expanding Functionality of the .iloc[] Attribute

Here, you will learn how to extract data that has specific conditions. Also, you will learn how to combine them and even create your own.

Setting Condition

Selecting Data Based on Condition

Dealing With Several Conditions

Making Your Code Beautiful

Conditions Quiz

In this section, you will expand your knowledge on setting different data conditions. You will learn to check if your data is in a defined list of values or between two values. You will also learn how to find the largest and smallest values.

Is Data in ...?

Combining Your Knowledge

Between Method

Extracting Specific Data

Finding the Smallest Values of a Column

Finding the Largest Values of a Column

Finding the Correlation

This section is one of the most fascinating of the course. Here, you will learn how to group data in different ways. It will help you work as a data analyst to find out information on specific data groups.

Getting Familiar With the .groupby() Method

Grouping by Several Columns

Complicated Grouping

Advanced Grouping

Dealing With Pivot Tables

Creating a Pivot Table

This section is one of the most significant for a data analyst because if the data contains missing data values in the incorrect format, it will be impossible to work with. Thus, you will learn how to deal with such inappropriate values here.

Checking for Missing Values

Calculating the Number of Missing Values

What Will We Do With the NaN Values?

How to Delete Only NaN Values?

Filling In the Missing Values

Managing Categorical Variables

Checking the Column Type

Managing an Incorrect Column

Renaming the Column

Introduction

DataFrames

Import and Export Files

Filtering the DataFrame

Grouping in Pandas

Data Cleaning

Merging in Pandas

Let's start with some basic definitions and concepts we'll use later. Consider the idea of a function, a numerical sequence, and its sum, and also understand what a coordinate system's basis is.

What is a Function?

What are Number Series?

Sum of Elements of The Series

Challenge: Calculating Sum of Geometric Progression

Vectors and Matrices

The simplest and most commonly used type of relationship is the linear relationship. Linear algebra is a branch of higher mathematics entirely devoted to linear functions and linear spaces. Let's look at some of the most important topics in linear algebra: vectors, matrices, solving linear equations, and solving the spectral problem for matrices.

Numerical Operations on Vectors and Matrices

Challenge: Calculate the Matrix Multiplication Result

Matrix Determinant

Scaling Factor of the Linear Transformation

Challenge: Figures' Linear Transformations

Inversed and Transposed Matrices

System of Linear Equations

Challenge: Solving the Task Using SLE

Eigenvalues and Eigenvectors

Mathematical analysis is a discipline that allows you to analyze functions according to various criteria. Consider how to check numerical sequences for convergence, find the maximum/minimum values of functions, solve nonlinear equations, and use integrals to solve applied problems.

Derivative of the Function

Partial Derivative of the Function

Challenge: Solving Task Using Derivative

Optimization Problem

Challenge: Solving the Optimisation Problem

Gradient Descent Method

Challenge: Optimising Function Of Multiple Variables

We will start our way of learning probability theory by considering some basic definitions and rules: what is a stochastic experiment and random event, what is independence and incompatibility of events in the context of probability theory, what is the probability and how can we calculate probabilities of different elementary events.

Stochastic Experiment and Random Event

Probability and It's Properties

Geometrical Probability

Challenge: Solving the Task Using Geometric Probability

Independence and Incompatibility of Random Events

Conditional Probability

In real-life tasks, we often have to deal with complex relationships and, as a result, calculate probabilities of several events or events that depend on each other. Let's consider how we can do this using probability theory.

Inclusion-Exclusion Principle

Challenge: Solving the Task Using Inclusion-Exclusion Principle

The Multiplication Rule of Probability

Law of Total Probability

Bayes' Theorem

Challenge: Solving the Task Using Bayes' Theorem

To solve many real problems in probability theory, special models have been created that describe a particular situation. Let's consider some of the most used models that can be used to describe some discrete results of stochastic experiments.

Binomial Distribution

Challenge: Solving Task Using Binomial Distribution

Multinomial Distribution

Geometric Distribution

Poisson Distribution

Challenge: Solving Task Using Poisson Distribution

What if the result of a stochastic experiment cannot be described by a discrete value? For this, models that work with continuous values are used. Consider the most popular of these models.

Continuous Uniform Distribution

Exponential Distribution

Challenge: Solving Task Using Exponential Distribution

Gaussian Distribution

Challenge: Solving Task Using Gaussian Distribution

Often we are faced with the task of checking the dependence of the results of different stochastic experiments on each other. Moreover, it is necessary not only to assess the presence of dependencies but also to somehow quantify the degree of dependencies. To solve these problems, we can use covariance and correlation.

What is Covariance?

What is Correlation?

Challenge: Solving the Task Using Correlation

Discover core statistical principles, including data types, measures of central tendency, and key differences between samples and populations.

Median Value of the Even Number of Values

Mean or Median

Mode Value

Descriptive Statistics Quiz

Learn to calculate and interpret mean, median, and mode using Python. Practice these operations with pandas to analyze real datasets.

Examine the Dataset

Calculating Mean and Median Values with Python

Statistics with pandas

Calculate the Mean and Median Salary

Understand how variance and standard deviation measure data spread. Learn to compute both manually and using Python tools.

Population Variance

Sample Variance

Calculate Variance with Python

Standard Deviation

Standard Deviation with Python

Calculating Variance and Standard Deviation

Explore how covariance and correlation describe relationships between variables. Practice calculating and comparing both metrics in Python.

Covariance

Correlation

Covariance and Correlation Quiz

Calculate Covariance and Correlation

Master confidence intervals to estimate population parameters. Use NumPy, pandas, and visualization libraries to calculate and interpret intervals with real data.

Explore the Data Set

Confidence Interval

Calculating Confidence Interval with Python

Confidence Interval Width Quiz

Calculate 95% Confidence Interval

Advanced Confidence Interval Calculation with Python

Match the Functions

Learn the fundamentals of hypothesis testing and the t-test. Understand how to design, perform, and interpret tests using Python to support data-driven decisions.

What is t-test

Hypotheses

t-test Mathematically

One-Tailed And Two-Tailed Test

t-test Assumptions

Performing a t-test in Python

Conduct a t-test

Paired t-test

Now we will understand some fundamental theoretical concepts which are used in solving real live tasks: absolutely continuous and discrete random variables, probability density function, cumulative distribution function, the characteristics of a random variable, etc.

Course Overview

Absolutely Continuous and Discrete Random Variables

Cumulative Distribution Functions and Probability Density Functions

Characteristics of Random Variables

Random Vectors

Useful Properties of the Gaussian Distribution

Challenge: Detecting Outliers Using 3-Sigma Rule

The limit theorems of probability theory are fundamental laws of probability theory that are often used in practice in a wide variety of areas, such as: building confidence intervals, estimating distribution parameters, providing A/B testings, creating ensembles of ML models, etc. Now we will consider two of the most commonly used: the Law of Large Numbers and the Central Limit Theorem.

Law of Large Numbers

Law of Large Numbers for Bernoulli Process

Challenge: Estimate Mean Value Using Law of Large Numbers

Central Limit Theorem

Challenge: Application of the CLT to Solving Real Problem

When we work with real data we usually do not know from which distribution this data was obtained. In order to determine this, we must be able to correctly estimate the parameters of this distribution and the type of distribution, which we will learn to do in this section.

General population. Samples. Population parameters.

Momentum estimation. Maximum Likelihood Estimation

Challenge: Estimate Parameters of Chi-square Distribution

Unbiased Estimation

Challenge: Checking Bias of An Estimation Using Simulation

Consistent Estimation

Efficient Estimation

Confidence Intervals for Population Parameters

Challenge: Confidence Interval for Exponential Distribution Parameter

We have already learned how to estimate the parameters of the population. But to estimate the parameter, we make an assumption about the population distribution. Can we say that our assumption is correct? How do we prove that the estimated parameters are the real parameters of the population? Can we show that two sets of samples are independent? To answer these questions, it is necessary to consider the concept of hypothesis testing.

What is Statistic Hypothesis? Type 1 and Type 2 Errors

What is P-value?

Comparing Means of Two Different Datasets

Challenge: Using CLT to Compare Mean Values of Non-Gaussian Datasets

Challenge: Resampling Approach to Compare Mean Values of the Datasets

Testing the Hypothesis of Independence of Two Random Variables

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