トラック Preparation for Data Science - 修了証付きオンライン学習

Preparation for Data Science

Intermediate

4.1

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Updated 08/2025

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

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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

pandasライブラリの基本を学びます。SeriesおよびDataFrameの作成と操作、その構造の探索、基本的なデータ操作の実践に自信を持つことができます。

CSVファイルおよびTXTファイルからデータを読み込み、管理する方法を習得します。pandasツールを使用してデータセットをインポートし、さらなる分析のために準備する実践的な経験を得られます。

データを効果的に探索、クリーンアップ、要約する方法を学びます。欠損値の処理方法、インサイトの抽出、pandasを用いた基本的な統計操作について理解します。

データの表示

クイズ：head()の使用

クイズ：head()、tail()、および sample()

このセクションでは、タイトルやインデックスによって特定の列を出力する方法を学びます。また、インデックスによって行を選択する方法についても理解を深めます。

Outputting Columns by Title

インデックスによる行の出力

特定の行と列の選択

インデックス化についてさらに学ぶ

Learning About lambda Functions

Lambda関数に慣れる

Iloc[]属性の機能拡張

ここでは、特定の条件を持つデータを抽出する方法を学びます。また、それらを組み合わせる方法や独自の条件を作成する方法も学びます。

このセクションでは、さまざまなデータ条件の設定に関する知識を拡張します。データが定義された値のリスト内にあるか、2つの値の間にあるかを確認する方法を学びます。また、最大値と最小値を見つける方法についても学びます。

このセクションは、本コースの中でも特に興味深い内容の一つです。ここでは、データをさまざまな方法でグループ化する方法を学びます。特定のデータグループに関する情報を見つけるために、データアナリストとして役立つ知識を身につけることができます。

このセクションはデータアナリストにとって最も重要なものの一つです。なぜなら、データに不適切な形式の欠損値が含まれている場合、作業が不可能になるためです。ここでは、そのような不適切な値への対処方法を学びます。

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

データ型、代表値、サンプルと母集団の主な違いなど、統計学の基本原則を学びます。

Pythonを使用して平均値、中央値、最頻値を計算し解釈する方法を学習します。pandasを用いて実際のデータセットでこれらの操作を練習します。

分散と標準偏差がデータのばらつきをどのように測定するかを理解します。手動およびPythonツールを使用して両方を計算する方法を学びます。

共分散と相関が変数間の関係をどのように表すかを探求します。Pythonで両方の指標を計算し比較する練習を行います。

信頼区間を習得し、母集団パラメータを推定します。NumPy、pandas、および可視化ライブラリを使用して、実データで区間を計算し解釈します。

仮説検定とt検定の基礎を学習します。データに基づいた意思決定を支援するために、Pythonを用いて検定を設計・実施・解釈する方法を理解します。

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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