- 1 Instructor: Career 365 Team
- 2 What is Data Science?
- 3 Who is this course for?
- 4 What will you need for this course?
- 5 What will You learn from this course?
- 6 Udemy Data Science Course Content Review:
- 7 Pricing:
- 8 Pros and Cons:
- 9 Conclusion
While there are a lot of data science courses out available on the internet, finding the right course that will help you get started might be a time-consuming process. In this review, we will take a look at “The Data Science Course 2021: Complete Data Science Bootcamp” available on Udemy.
If you are unsure whether or not you should buy this course, we will help you decide by taking a look at what this course offers. With more than 366,220 students enrolled in this course & it has a rating of 4.6 out of 5, with more than 85,719 ratings at the time of writing.
The course is updated every year, it was last updated in January of 2021. It is published on Udemy by the course creator 365 Careers and the 365 Careers Team. That being said, let us take a look at who the instructors are.
Instructor: Career 365 Team
The instructor of this course is a company called 365 Careers. They are the #1 best seller of finance-related courses on the platform. With more than 1,000,000 students enrolled in their courses in more than 210 countries, students from various companies such as Apple and PayPal have used courses by the Career 365 Team.
What is Data Science?
Data Science is aggravating data from various sources and using different methods, algorithms, systems, and processes to make use of the information we collected and gain some knowledge from it. The data can either be structured or unstructured.
It is usually associated with machine learning, big data, and data mining. Using a combination of mathematics, statistics, and computer science, data science is aimed to extract useful information from a huge amount of data.
Who is this course for?
If you want to learn the basics of data science and get familiar with all the basics of Data Science and want to pursue a career in the field, then this course is for you. This course will take you through all the terminologies and technologies used by data scientists.
It is also suited for people who are just getting started with the fundamentals of data science and scale up their skills. This course acts as a complete toolbox in order to become a data scientist.
What will you need for this course?
You can opt for this course even if you have no technical expertise or knowledge about data science. However, you will need to install Anaconda and have Microsoft Excel installed. (Microsoft excel 2003 and later is supported).
What will You learn from this course?
- Mathematical knowledge required for Machine Learning
- Perform linear and logistic regressions in Python
- Make use of NumPy, statsmodels, and scikit-learn in Python to create machine learning algorithms
- Improve existing ML algorithms by studying various techniques such as overfitting, underfitting, validation, training, n-fold cross-validation, testing, and how to make use of hyperparameters to improve performance.
- Learning how to pre-process data
- Learn to use Python and use it for statistical analysis
- Perform Cluster and Factor analysis
- Apply whatever you have learned in real-life scenarios
- Using deep neural networks
- Deep Learning using TensorFlow
- Python programming using matplotlib, Seaborn, Advanced statistical analysis, machine learning with stats models, scikit-learn, and pandas.
Now that we know what is needed to take this course and what it has to offer, let us review the content of “The Data Science Course 2021: Complete Data Science Bootcamp”.
Udemy Data Science Course Content Review:
1. Intro to Data and Data Science
You will learn about what data science is in theory and learn why almost every business on the planet is looking forward to making use of data science to boost their business.
Moreover, you will learn about other things such as business intelligence, artificial intelligence, and machine learning. The video titled “Introduction to data and data science” will make you familiar with all the terminologies and buzzwords used in the field.
The second section of the course takes you through all the things used by data scientists in Probability such as Bayesian Interface, Distributions, Combinatronics along with all the basics of probability.
It is one of the courses that includes teaching all the mathematical aspects of data science. In the last subsection of this section, you will learn about how Probability is used in other fields such as statistics, finance, and data science.
Statistics is a big part of learning data science. This section starts by introducing what statistics is and how you will use it in data science by using examples.
You then proceed to cover various parts of statistics and learn advanced statistics with every topic using practical examples. There are various things this section will help you learn such as descriptive statistics and inferential statistics.
It then moves on to Hypothesis testing and how it is used in data science using an example.
4. Introduction to Python
Python is one of the most flexible languages that can be used in everything and anything including data science, general programming, and machine learning. This section of the course starts by introducing users to Python and why you should use Python and Jupyter.
It will also help you install these tools and help users understand the Jupyter dashboard. The last subsection deals with the prerequisites for coding in Jupyter and get familiar with the interface.
You will then learn about the several Data Types available in Python and how you can use Variables. The next part involves making use of basic Python Syntax followed by advanced Python Operators. You will also learn about Conditional Statements, Functions, Sequences, and Iterations.
The section ends by introducing users to Advanced Python Tools. You will learn what OOPS is, how to use modules and packages. Users will also learn about the Standard Python Library and how to import modules in Python.
5. Advanced Statistical Methods in Python
Starting with an introduction to Regression Analysis, this section proceeds and helps users learn about linear regression, multiple linear regression and ends with logistic regression.
Once you are familiar with all types of regressions, the course helps you how to use advanced statistical models using Linear regression with Sklearn, multiple linear regression with stats models.
It then proceeds to explain other advanced statistical methods such as Logistics Regression, Cluster Analysis, K-Means Clustering, and different types of clustering such as Dendrogram.
If you are new to data science, you will need to learn various mathematical topics. In case you haven’t studied engineering or mathematics, this section is designed to help you understand all the mathematical concepts used in data science such as Matrix, Scalars, Vectors, Linear Algebra, Tensor, and Geometry.
The section ends with the course teaching users how to perform several operations on Matrix such as Addition, Subtraction, and how to Transpose a matrix. It also helps you resolve the several errors experienced when adding a matrix.
7. Deep Learning
The last section of the course teaches users about Deep Learning. It starts with a video titled “What to expect from this part?”, followed by an introduction to what Neural Networks are. You will also learn how to build a simple Neural Network using NumPy.
Also, you will also learn about what TensorFlow 2.0 does and how to use it for various purposes.
It then proceeds with the introduction of Deep Neural Networks. During the process, you will also learn how to install Glorot, also known as Xavier. The course lets users get familiar with Preprocessing and help classify on the MNIST Dataset.
It also helps users apply the knowledge learned using an example. The section ends with a summary of whatever you have learned and an overview of Convolutional Neural Networks.
8. Case Studies
There are several case studies included in the course including:
- Preprocessing the “Absenteeism_data”
- Applying Machine Learning to Create the “Absenteeism_module”
- Loading the “Absenteeism_module”
- Analyzing the Predicted Outputs in Tableau
The course is priced at approximately $120. Taking into consideration the content of the course, it is reasonably priced. Check the discounted offer from here.
The content creator also offers a 30-day moneyback guarantee in case you find that the course is not right for you.
Pros and Cons:
The course offers all the basics that you will ever need to get started getting into Data Science. After taking this course, you can easily proceed to take advanced Machine Learning and Artificial Intelligence courses.
This is the best foundation course you will ever need to learn everything about data science. The only downside to this course is the high price of the course.
With half of the students rating the course as 5 stars. the course has an average of 4.6 stars and over 366,220 students at the time of writing. It is one of the best-selling courses on Udemy and is developed keeping in mind that even people without any technical experience can learn about Data Science.
If you are looking forward to working as a Data scientist and include various skills such as Statistical Analysis, Python programming using NumPy, SeaBorn, etc., Advanced Statistical Analysis, Tableau, and Machine Learning with stats models and Scikit-learn, then this course is for you.
All-in-all, this is the best Bootcamp course you will ever need to learn everything there is about data science.