Preprocessing Data with NumPy
Preprocessing Data with NumPy, available at $79.99, has an average rating of 4.59, with 78 lectures, 2 quizzes, based on 168 reviews, and has 1392 subscribers.
You will learn about Arrays. The definition of a package/library. Installing and Upgrading a package. Navigating the documentation. A history of NumPy. The relationship between arrays and vectors. Arrays vs Lists. Indexing. Assigning values to arrays. Elementwise properties and operations. Datatypes supported by ndarrays. Broadcasting and type casting. Running a function or method over a given axis. Slicing, Stepwise Slicing, Conditional Slicing Dimensionality reduction in arrays. Generating arrays full of identical values. Generating non-random sequences of data. Generating random data with Random Generators. Generating random samples from a random probability distribution. Importing and exporting data with and from NumPy. NPY and NPZ files. Maximums and Minimums. Percentiles and Quantiles. Mean and Variance. Covariance and Correlation. Calculating histograms. Higher dimension histograms. Finding and filling up missing values. Substituting "filler" values. Reshaping arrays. Removing parts of arrays. Removing parts of individual elements within arrays. (Stripping) Sorting and Shuffling. Argument Functions. Stacking and Concatenating. Finding the unique values within an array. A comprehensive practical example of data cleaning and preprocessing. This course is ideal for individuals who are Aspiring data analysts. or Programming beginners. or People interested in analyzing data through Python. or Analysts who wish to specialize in Python. or Finance graduates and professionals who need to better apply their knowledge in Python. It is particularly useful for Aspiring data analysts. or Programming beginners. or People interested in analyzing data through Python. or Analysts who wish to specialize in Python. or Finance graduates and professionals who need to better apply their knowledge in Python.
Enroll now: Preprocessing Data with NumPy
Summary
Title: Preprocessing Data with NumPy
Price: $79.99
Average Rating: 4.59
Number of Lectures: 78
Number of Quizzes: 2
Number of Published Lectures: 77
Number of Published Quizzes: 2
Number of Curriculum Items: 80
Number of Published Curriculum Objects: 79
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- Arrays.
- The definition of a package/library.
- Installing and Upgrading a package.
- Navigating the documentation.
- A history of NumPy.
- The relationship between arrays and vectors.
- Arrays vs Lists.
- Indexing.
- Assigning values to arrays.
- Elementwise properties and operations.
- Datatypes supported by ndarrays.
- Broadcasting and type casting.
- Running a function or method over a given axis.
- Slicing, Stepwise Slicing, Conditional Slicing
- Dimensionality reduction in arrays.
- Generating arrays full of identical values.
- Generating non-random sequences of data.
- Generating random data with Random Generators.
- Generating random samples from a random probability distribution.
- Importing and exporting data with and from NumPy.
- NPY and NPZ files.
- Maximums and Minimums.
- Percentiles and Quantiles.
- Mean and Variance.
- Covariance and Correlation.
- Calculating histograms.
- Higher dimension histograms.
- Finding and filling up missing values.
- Substituting "filler" values.
- Reshaping arrays.
- Removing parts of arrays.
- Removing parts of individual elements within arrays. (Stripping)
- Sorting and Shuffling.
- Argument Functions.
- Stacking and Concatenating.
- Finding the unique values within an array.
- A comprehensive practical example of data cleaning and preprocessing.
Who Should Attend
- Aspiring data analysts.
- Programming beginners.
- People interested in analyzing data through Python.
- Analysts who wish to specialize in Python.
- Finance graduates and professionals who need to better apply their knowledge in Python.
Target Audiences
- Aspiring data analysts.
- Programming beginners.
- People interested in analyzing data through Python.
- Analysts who wish to specialize in Python.
- Finance graduates and professionals who need to better apply their knowledge in Python.
The problem
Most data analyst, data science, and coding courses miss a crucial 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 favor. 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 using the NumPy package. This course will turn you into capable data analyst with a fantastic understanding of one of the most prominent computing packages in the world. To take you there, we will cover the following topics extensively.
· The ndarray class and why we use it
· The type of data arrays usually contain
· Slicing and squeezing datasets
· Dimensions of arrays, and how to reduce them
· Generating pseudo-random data
· Importing data from external text files
· Saving/Exporting data to external files
· Computing the statistics of the dataset (max, min, mean, variance, etc.)
· Data cleaning
· Data preprocessing
· 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 concatenate datasets before you know how to index or slice them.
So, to prepare you for the long journey towards a data science position, we created a course that will show you all the tools for the job: The Preprocessing Data with NumPy course [MG1] .
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.
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. What’s more, 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 great flexibility and can take your analysis to the next level.
Some of the topics we will cover:
1. Fundamentals of NumPy
2. Random Generators
3. Working with text files
4. Statistics with NumPy
5. Data preprocessing
6. Final practical example
1. Fundamentals of NumPy
To fully grasp the capabilities of NumPy, we need to start from the fundamentals. In this part of the course, we’ll examine the ndarray class, discuss why it’s so popular and get familiar with terms like “indexing”, “slicing”, “dimensions” and “reducing”.
Why learn it?
As stated above, NumPy is the quintessential package for scientific computing, and to understand its true value, we need to start from its very core – the ndarray class. The better we comprehend the basics, the easier it’s going to be to grasp the more difficult concepts. That’s why it’s fundamental to lay a good foundation on which to build our NumPy skills.
2. Random Generators
After we’ve learned the basics, we’ll move on to pseudo-random data and random generators. These generators will help construct a set of arbitrary variables from a given probability distribution, or a fixed set of viable options.
Why learn it?
Working in a data-driven field, we sometimes need to construct partially arbitrary tests to see if our code works as intended. And here lies the value of random generators, as they allow us to construct datasets of pseudo-random data. The added benefit of random generators is that we can set a seed if we wish to replicate a particular randomization, but we’ll go into all the details in the course itself.
3. 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 covered 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.
4. Statistics with NumPy
Once we’ve learned how to import large sets of information from external text files, we’ll finally be ready to explore one of NumPy’s strengths – statistics. Since the package is extremely computationally durable, we often rely on its functions and methods to calculate the statistics of a sample dataset. These include the likes of the mean, the standard deviation, and much more.
Why learn it?
To become a data scientist, you not only need to be able to preprocess a dataset, but also to extract valuable insights. One way to learn more about a dataset is by examining its statistics. So, we’ll use the package to understand more about the data and how to convert this knowledge into crucial information we can use for forecasting.
5. Data preprocessing
Even when your dataset is in clean and comprehensible 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.
6. Practical example
The course contains plenty of exercises and practical cases. What’s more, 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 on mastering NumPy in your pursuit of a data career.
What you get
· Active Q&A support
· All the NumPy 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 NumPy 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.
Course Curriculum
Chapter 1: Introduction to NumPy
Lecture 1: What Does the Course Cover?
Lecture 2: Download All Resources
Lecture 3: FAQ
Lecture 4: The NumPy Package and Its Applications
Lecture 5: Installing and Upgrading NumPy
Lecture 6: What is an array?
Lecture 7: Using the NumPy Documentation
Lecture 8: Introduction to NumPy – Exercise
Chapter 2: Why Do We Use NumPy?
Lecture 1: A Brief History of NumPy
Lecture 2: ndarrays
Lecture 3: Arrays vs Lists
Lecture 4: Why Do We Use NumPy – Exercise
Chapter 3: NumPy Fundamentals
Lecture 1: Indexing
Lecture 2: Assigning Values
Lecture 3: Elementwise Properties
Lecture 4: NumPy Datatypes
Lecture 5: Characteristics of NumPy Functions – Part 1
Lecture 6: Characteristics of NumPy Functions – Part 2
Lecture 7: NumPy Fundamentals – Exercise
Chapter 4: Working with Arrays
Lecture 1: Basic Slicing
Lecture 2: Stepwise Slicing
Lecture 3: Conditional Slicing
Lecture 4: Dimensions and the Squeeze Function
Lecture 5: Working with Arrays – Exercise
Chapter 5: Generating Data with NumPy
Lecture 1: Empty Arrays, Arrays of Identical Values
Lecture 2: _like Functions
Lecture 3: A Sequence of Numbers – np.arange()
Lecture 4: Random Generators and Seeds
Lecture 5: Random Integers, Probabilities and Choices
Lecture 6: Random Probability Distributions
Lecture 7: Applications of Random Generators
Lecture 8: Generating Data with NumPy – Exercise
Chapter 6: Importing and Saving Data
Lecture 1: Importing Data with Numpy – np.loadtxtx() vs np.genfromtxt()
Lecture 2: Importing Data with NumPy – Simple Cleaning when Importing
Lecture 3: Importing Data with NumPy – String vs Object vs Numbers
Lecture 4: Importing Data with NumPy – Exercise
Lecture 5: Saving Data with NumPy – NPY
Lecture 6: Saving Data with NumPy – NPZ
Lecture 7: Saving Data with NumPy – CSV
Lecture 8: Importing and Saving Data – Exercise
Chapter 7: Statistics with NumPy
Lecture 1: Using NumPy Statistical Functions
Lecture 2: Minimal and Maximal Values
Lecture 3: Percentiles and Quantiles
Lecture 4: Averages and Variance
Lecture 5: Covariance and Correlation
Lecture 6: Histogram – Part 1: 1-D Histograms
Lecture 7: Histogram – Part 2: Higher Dimension Histograms
Lecture 8: N-A-N Equivalent Functions
Lecture 9: Statistics with NumPy – Exercise
Chapter 8: Manipulation Data with NumPy
Lecture 1: Checking for Missing Values
Lecture 2: Substituting Filler Values
Lecture 3: Reshaping Arrays
Lecture 4: Removing Values
Lecture 5: Sorting Arrays
Lecture 6: Argument Functions – Part 1: Argument Sort
Lecture 7: Argument Functions – Part 1: Argument Where
Lecture 8: Shuffling Data
Lecture 9: Casting Arrays
Lecture 10: Stripping Symbols from Arrays
Lecture 11: Stacking Arrays
Lecture 12: Concatenating Arrays
Lecture 13: Finding Unique Values in Arrays
Chapter 9: A NumPy Practical Example
Lecture 1: Setting Up: Introduction to the Practical Example
Lecture 2: Setting Up: Importing the Data Set
Lecture 3: Setting Up: Checking for Incomplete Data
Lecture 4: Setting Up: Splitting the Dataset
Lecture 5: Setting Up: Creating Checkpoints
Lecture 6: Manipulating Text Data: Issue Date
Lecture 7: Manipulating Text Data: Loan Status and Term
Lecture 8: Manipulating Text Data: Grade and Sub Grade
Lecture 9: Manipulating Text Data: Verification Status & URL
Lecture 10: Manipulating Text Data: State Address
Lecture 11: Manipulating Text Data: Converting Strings and Creating a Checkpoint
Lecture 12: Manipulating Numeric Data: Substitute Filler Values
Lecture 13: Manipulating Numeric Data: Currency Change – The Exchange Rate
Lecture 14: Manipulating Numeric Data: Currency Change – From USD to EUR
Lecture 15: Completing the Dataset
Instructors
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- 1 stars: 0 votes
- 2 stars: 1 votes
- 3 stars: 9 votes
- 4 stars: 45 votes
- 5 stars: 113 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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