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The Data Analyst Course: Complete Data Analyst Bootcamp 2021

Complete Data Analyst Training: Python, NumPy, Pandas, Data Collection, Preprocessing, Data Types, Data Visualization
Bestseller
Rating: 4.6 out of 54.6 (1,148 ratings)
26,486 students
Created by 365 Careers
Last updated 2/2021
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • The course provides the complete preparation you need to become a data analyst
  • Fill up your resume with in-demand data skills: Python programming, NumPy, pandas, data preparation - data collection, data cleaning, data preprocessing, data visualization; data analysis, data analytics
  • Acquire a big picture understanding of the data analyst role
  • Learn beginner and advanced Python
  • Study mathematics for Python
  • We will teach you NumPy and pandas, basics and advanced
  • Be able to work with text files
  • Understand different data types and their memory usage
  • Learn how to obtain interesting, real-time information from an API with a simple script
  • Clean data with pandas Series and DataFrames
  • Complete a data cleaning exercise on absenteeism rate
  • Expand your knowledge of NumPy – statistics and preprocessing
  • Go through a complete loan data case study and apply your NumPy skills
  • Master data visualization
  • Learn how to create pie, bar, line, area, histogram, scatter, regression, and combo charts
  • Engage with coding exercises that will prepare you for the job
  • Practice with real-world data
  • Solve a final capstone project
Curated for the Udemy for Business collection

Requirements

  • No prior experience is required. We will start from the very basics
  • You’ll need to install Anaconda. We will show you how to do that step by step

Description

The problem

Most data analyst, data science, and coding courses miss a critical practical step. They don’t teach you how to work with raw data, how to clean, and preprocess it. This creates a sizeable gap between the skills you need on the job and the abilities you have acquired in training. Truth be told, real-world data is messy, so you need to know how to overcome this obstacle to become an independent data professional.

The bootcamps we have seen online and even live classes neglect this aspect and show you how to work with ‘clean’ data. But this isn’t doing you a favour. In reality, it will set you back both when you are applying for jobs, and when you’re on the job.

The solution

Our goal is to provide you with complete preparation. And this course will turn you into a job-ready data analyst. To take you there, we will cover the following fundamental topics extensively.

  • Theory about the field of data analytics

  • Basic Python

  • Advanced Python

  • NumPy

  • Pandas

  • Working with text files

  • Data collection

  • Data cleaning

  • Data preprocessing

  • Data visualization

  • Final practical example

Each of these subjects builds on the previous ones. And this is precisely what makes our curriculum so valuable. Everything is shown in the right order and we guarantee that you are not going to get lost along the way, as we have provided all necessary steps in video (not a single one skipped). In other words, we are not going to teach you how to analyse data before you know how to gather and clean it.

So, to prepare you for the entry-level job that leads to a data science position - data analyst - we created The Data Analyst Course.

This is a rather unique training program because it teaches the fundamentals you need on the job. A frequently neglected aspect of vital importance.

Moreover, our focus is to teach topics that flow smoothly and complement each other. The course provides complete preparation for someone who wants to become a data analyst at a fraction of the cost of traditional programs (not to mention the amount of time you will save). We believe that this resource will significantly boost your chances of landing a job, as it will prepare you for practical tasks and concepts that are frequently included in interviews.

The topics we will cover

1. Theory about the field of data analytics

2. Basic Python

3. Advanced Python

4. NumPy

5. Pandas

6. Working with text files

7. Data collection

8. Data cleaning

9. Data preprocessing

10. Data visualization

11. Final practical example


1. Theory about the field of data analytics

Here we will focus on the big picture. But don’t imagine long boring pages with terms you’ll have to check up in a dictionary every minute. Instead, this is where we want to define who a data analyst is, what they do, and how they create value for an organization.

Why learn it?

You need a general understanding to appreciate how every part of the course fits in with the rest of the content. As they say, if you know where you are going, chances are that you will eventually get there. And since data analyst and other data jobs are relatively new and constantly evolving, we want to provide you with a good grasp of the data analyst role specifically. Then, in the following chapters, we will teach you the actual tools you need to become a data analyst.

2. Basic Python

This course is centred around Python. So, we’ll start from the very basics. Don’t be afraid if you do not have prior programming experience.

Why learn it?

You need to learn a programming language to take full advantage of the data-rich world we live in. Unless you are equipped with such a skill, you will always be dependent on other people’s ability to extract and manipulate data, and you want to be independent while doing analysis, right? Also, you don’t necessarily need to learn many programming languages at once. It is enough to be very skilled at just one, and we’ve naturally chosen Python which has established itself as the number one language for data analysis and data science (thanks to its rich libraries and versatility).

3. Advanced Python

We will introduce advanced Python topics such as working with text data and using tools such as list comprehensions and anonymous functions.

Why learn it?

These lessons will turn you into a proficient Python user who is independent on the job. You will be able to use Python’s core strengths to your advantage. So, here it is not just about the topics, it is also about the depth in which we explore the most relevant Python tools.

4. NumPy

NumPy is Python’s fundamental package for scientific computing. It has established itself as the go-to tool when you need to compute mathematical and statical operations.

Why learn it?

A large portion of a data analyst’s work is dedicated to preprocessing datasets. Unquestionably, this involves tons of mathematical and statistical techniques that NumPy is renowned for. In addition, the package introduces multi-dimensional array structures and provides a plethora of built-in functions and methods to use while working with them. In other words, NumPy can be described as a computationally stable state-of-the-art Python instrument that provides flexibility and can take your analysis to the next level.

5. Pandas

The pandas library is one of the most popular Python tools that facilitate data manipulation and analysis. It is very valuable because you can use it to manipulate all sorts of information - numerical tables and time series data, as well as text.

Why learn it?

Pandas is the other main tool an analyst needs to clean and preprocess the data they are working with. Its data manipulation features are second to none in Python because of the diversity and richness it provides in terms of methods and functions. The combined ability to work with both NumPy and pandas is extremely powerful as the two libraries complement each other. You need to be capable to operate with both to produce a complete and consistent analysis independently.

6. Working with text files

Exchanging information with text files is practically how we exchange information today. In this part of the course, we will use the Python, pandas, and NumPy tools learned earlier to give you the essentials you need when importing or saving data.

Why learn it?

In many courses, you are just given a dataset to practice your analytical and programming skills. However, we don’t want to close our eyes to reality, where converting a raw dataset from an external file into a workable Python format can be a massive challenge.

7. Data collection

In the real world, you don’t always have the data readily available for you. In this part of the course, you will learn how to retrieve data from an API.

Why learn it?

You need to know how to source your data, right? To be a well-rounded analyst you must be able to collect data from outside sources. This is rarely a one-click process. This section aims at providing you with all the necessary tools to do that on your own.

8. Data cleaning

The next logical step is to clean your data. This is where you will apply the pandas skills acquired earlier in practice. All lessons throughout the course have a real-world perspective.

Why learn it?

A large part of a data analyst’s job in the real world involves cleaning data and preparing it for the actual analysis. You can’t expect that you’ll deal with flawless data sources, right? So, it will be up to you to overcome this stage and clean your data.

9. Data preprocessing

Even when your dataset is clean and in an understandable shape, it isn’t quite ready to be processed for visualizations and analysis just yet. There is a crucial step in between, and that’s data preprocessing.

Why learn it?

Data preprocessing is where a data analyst can demonstrate how good or great they are at their job. This stage of the work requires the ability to choose the right statistical tool that will improve the quality of your dataset and the knowledge to implement it with advanced pandas and NumPy techniques. Only when you’ve completed this step can you say that your dataset is preprocessed and ready for the next part, which is data visualization.

10. Data visualization

Data visualization is the face of data. Many people look at the data and see nothing. The reason for that is that they are not creating good visualizations. Or even worse – they are creating nice graphs but cannot interpret them accurately.

Why learn it?

This part of the course will teach you how to use your data to produce meaningful insights. At the end of the day, data charts are what conveys the most information in the shortest amount of time. And nothing speaks better than a well crafted and meaningful data visualization.

11. Practical example

The course contains plenty of exercises and practical cases. In the end, we have included a comprehensive practical example that will show you how everything you have learned along the way comes nicely together. This is where you will be able to appreciate how far you have come in your journey to becoming a data analyst and starting your data career.

What you get

  • A program worth $1,250

  • Active Q&A support

  • All the knowledge to become a data analyst

  • A community of aspiring data analysts

  • A certificate of completion

  • Access to frequent future updates

  • Real-world training

  • Get ready to become a data analyst from scratch

Why wait? Every day is a missed opportunity.

Click the “Buy Now” button and become a part of our data analyst program today.

Who this course is for:

  • You should take this course if you want to become a Data Analyst and Data Scientist
  • This course is for you if you want a great career
  • The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills

Featured review

Masrur Rahman
Masrur Rahman
86 courses
10 reviews
Rating: 5.0 out of 52 months ago
This course walks one through python and data preprocessing from completely basic to intermediate level. I enjoyed learning the entire time. The lectures are very easy to follow due to the use of effective visual tools.

Course content

27 sections • 274 lectures • 20h 53m total length

  • Preview04:46
  • Preview05:36
  • Download All Resources
    00:15
  • FAQ
    09:59

  • Introduction to the World of Business and Data
    02:26
  • Relevant Terms Explained
    05:45
  • Data Analyst Compared to Other Data Jobs
    02:27
  • Data Analyst Job Description
    05:42
  • Why Python
    05:07

  • Preview01:24
  • Programming Explained in a Few Minutes
    05:04
  • Programming Explained in a Few Minutes
    2 questions
  • Jupyter - Introduction
    03:29
  • Jupyter - Installing Anaconda
    04:00
  • Jupyter - Intro to Using Jupyter
    03:10
  • Jupyter - Working with Notebook Files
    06:09
  • Jupyter - Using Shortcuts
    03:07
  • Jupyter - Handling Error Messages
    05:52
  • Jupyter - Restarting the Kernel
    02:17
  • Jupyter - Introduction
    5 questions

  • Preview03:37
  • Python Variables
    1 question
  • Preview03:05
  • Types of Data - Numbers and Boolean Values
    1 question
  • Types of Data - Strings
    05:40
  • Types of Data - Strings
    2 questions
  • Basic Python Syntax - Arithmetic Operators
    03:23
  • Basic Python Syntax - Arithmetic Operators
    1 question
  • Basic Python Syntax - The Double Equality Sign
    01:33
  • Basic Python Syntax - The Double Equality Sign
    1 question
  • Basic Python Syntax - Reassign Values
    01:08
  • Basic Python Syntax - Reassign Values
    1 question
  • Basic Python Syntax - Add Comments
    01:34
  • Basic Python Syntax - Add Comments
    1 question
  • Basic Python Syntax - Line Continuation
    00:49
  • Basic Python Syntax - Indexing Elements
    01:18
  • Basic Python Syntax - Indexing Elements
    1 question
  • Basic Python Syntax - Indentation
    01:44
  • Basic Python Syntax - Indentation
    1 question
  • Operators - Comparison Operators
    02:10
  • Operators - Comparison Operators
    2 questions
  • Operators - Logical and Identity Operators
    05:35
  • Operators - Logical and Identity Operators
    2 questions
  • Conditional Statements - The IF Statement
    03:01
  • Conditional Statements - The IF Statement
    1 question
  • Conditional Statements - The ELSE Statement
    02:45
  • Conditional Statements - The ELIF Statement
    05:34
  • Conditional Statements - A Note on Boolean Values
    02:13
  • Conditional Statements - A Note on Boolean Values
    1 question
  • Functions - Defining a Function in Python
    02:02
  • Functions - Creating a Function with a Parameter
    03:49
  • Functions - Another Way to Define a Function
    02:36
  • Functions - Using a Function in Another Function
    01:49
  • Functions - Combining Conditional Statements and Functions
    03:06
  • Functions - Creating Functions That Contain a Few Arguments
    01:17
  • Functions - Notable Built-in Functions in Python
    03:56
  • Functions
    1 question
  • Sequences - Lists
    04:02
  • Sequences - Lists
    1 question
  • Sequences - Using Methods
    03:19
  • Sequences - Using Methods
    1 question
  • Sequences - List Slicing
    04:31
  • Sequences - Tuples
    03:11
  • Sequences - Dictionaries
    04:04
  • Sequences - Dictionaries
    1 question
  • Iteration - For Loops
    02:56
  • Iteration - For Loops
    1 question
  • Iteration - While Loops and Incrementing
    02:26
  • Iteration - Create Lists with the range() Function
    03:49
  • Iteration - Create Lists with the range() Function
    1 question
  • Iteration - Use Conditional Statements and Loops Together
    03:11
  • Iteration - Conditional Statements, Functions, and Loops
    02:27
  • Iteration - Iterating over Dictionaries
    03:07

  • Object-Oriented Programming (OOP)
    05:00
  • Modules, Packages, and the Python Standard Library
    04:24
  • Importing Modules
    03:24
  • Introduction to Using NumPy and pandas
    09:09
  • What is Software Documentation?
    03:57
  • The Python Documentation
    06:23

  • What Is а Matrix?
    03:37
  • Scalars and Vectors
    02:58
  • Linear Algebra and Geometry
    03:06
  • Arrays in Python
    05:09
  • What Is a Tensor?
    03:00
  • Adding and Subtracting Matrices
    03:36
  • Errors When Adding Matrices
    02:01
  • Transpose
    05:13
  • Dot Product of Vectors
    03:48
  • Dot Product of Matrices
    08:23
  • Why is Linear Algebra Useful
    10:10

  • The NumPy Package and Why We Use It
    04:03
  • Installing/Upgrading NumPy
    02:01
  • Ndarray
    03:06
  • The NumPy Documentation
    04:42
  • NumPy Basics - Exercise
    00:15

  • Introduction to the pandas Library
    05:41
  • Installing and Running pandas
    05:57
  • Introduction to pandas Series
    08:41
  • Working with Attributes in Python
    05:22
  • Using an Index in pandas
    04:00
  • Label-based vs Position-based Indexing
    04:31
  • More on Working with Indices in Python
    05:37
  • Using Methods in Python - Part I
    04:55
  • Using Methods in Python - Part II
    02:36
  • Parameters vs Arguments
    04:35
  • the pandas Documentation
    09:54
  • Introduction to pandas DataFrames
    05:23
  • Creating DataFrames from Scratch - Part I
    05:56
  • Creating DataFrames from Scratch - Part II
    05:03
  • Additional Notes on Using DataFrames
    01:58
  • pandas Basics - Conclusion
    00:44

  • Working with Files in Python - An Introduction
    03:46
  • File vs File Object, Read vs Parse
    02:52
  • Structured vs Semi-Structured and Unstructured Data
    03:10
  • Data Connectivity through Text Files
    03:06
  • Principles of Importing Data in Python
    04:50
  • More on Text Files (*.txt vs *.csv)
    04:33
  • Fixed-width Files
    01:26
  • Common Naming Conventions Used in Programming
    03:49
  • Importing Text Files in Python ( open() )
    09:00
  • Importing Text Files in Python ( with open() )
    04:53
  • Importing *.csv Files with pandas - Part I
    05:35
  • Importing *.csv Files with pandas - Part II
    02:37
  • Importing *.csv Files with pandas - Part III
    05:57
  • Importing Data with the "index_col" Parameter
    02:35
  • Importing Data with NumPy - .loadtxt() vs genfromtxt()
    10:43
  • Importing Data with NumPy - Partial Cleaning While Importing
    07:21
  • Importing Data with NumPy - Exercise
    00:15
  • Importing *.json Files
    05:14
  • Prelude to Working with Excel Files in Python
    03:40
  • Working with Excel Data (the *.xlsx Format)
    01:55
  • An Important Exercise on Importing Data in Python
    05:44
  • Importing Data with the pandas' "Squeeze" Parameter
    02:37
  • A Note on Importing Files in Jupyter
    03:10
  • Saving Your Data with pandas
    03:11
  • Saving Your Data with NumPy - np.save()
    05:23
  • Saving Your Data with NumPy - np.savez()
    05:12
  • Saving Your Data with NumPy - np.savetxt()
    03:58
  • Saving Your Data with NumPy - Exercise
    00:15
  • Working with Text Files - Conclusion
    00:42

  • Working with Text Data and Argument Specifiers
    09:18
  • Manipulating Python Strings
    04:13
  • Using Various Python String Methods - Part I
    06:51
  • Using Various Python String Methods - Part II
    06:44
  • String Accessors
    04:49
  • Using the .format() Method
    09:02

Instructor

365 Careers
Creating opportunities for Business & Finance students
365 Careers
  • 4.5 Instructor Rating
  • 405,773 Reviews
  • 1,383,615 Students
  • 71 Courses

365 Careers is the #1 best-selling provider of finance courses on Udemy. The company’s courses have been taken by more than 1,000,000 students in 210 countries. People working at world-class firms like Apple, PayPal, and Citibank have completed 365 Careers trainings.  

Currently, the firm focuses on the following topics on Udemy:  

1) Finance – Finance fundamentals, Financial modeling in Excel, Valuation, Accounting, Capital budgeting, Financial statement analysis (FSA), Investment banking (IB), Leveraged buyout (LBO), Financial planning and analysis (FP&A), Corporate budgeting, applying Python for Finance, Tesla valuation case study, CFA, ACCA, and CPA

2) Data science – Statistics, Mathematics, Probability, SQL, Python programming, Python for Finance, Business Intelligence, R, Machine Learning, TensorFlow, Tableau, the integration of SQL and Tableau, the integration of SQL, Python, Tableau, Power BI, Credit Risk Modeling, and Credit Analytics

3) Entrepreneurship – Business Strategy, Management and HR Management, Marketing, Decision Making, Negotiation, and Persuasion, Tesla's Strategy and Marketing

4) Office productivity – Microsoft Excel, PowerPoint, Microsoft Word, and Microsoft Outlook

5) Blockchain for Business

All of the company’s courses are:  

Pre-scripted  

Hands-on  

Laser-focused  

Engaging  

Real-life tested  

By choosing 365 Careers, you make sure you will learn from proven experts, who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time.  

If you want to become a financial analyst, a finance manager, an FP&A analyst, an investment banker, a business executive, an entrepreneur, a business intelligence analyst, a data analyst, or a data scientist, 365 Careers’ courses are the perfect place to start. 

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