**Best Udemy courses for Data Science**:-

**Aspire to learn Data Science; But don’t know which way to follow? We will make it easy for you. Here are some of the best Udemy Data Science courses enlisted for you. **

Data Science is an analyst art of extracting meaning full information from a bunch of data by applying certain types of systems, algorithms, scientific methods, etc.

The data provided could be in a proper structure or possibly be unstructured, a data scientist applies those algorithms on the data set to extract and evaluate insights.

**1.** **The Data Science Course 2020: Complete Data Science Bootcamp**

This is one of the High rated courses on Udemy. It is designed to accelerate the career of the students who wish to be data scientists. This course can add many data science skills to your resume, like python, analyst skills, TensorFlow, etc. This course is full of real time questions that are currently been worked on in some of the fortune 500 companies like Microsoft, Amazon, Google, Airbnb, etc. that will help you gain the practical skills and problem solving approach to be followed.

**What you will Learn**:

- toolbox needed to be data scientist
- data science skills: Statistical analysis, Python with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, stats models, scikit-learn, TensorFlow
- Learn mathematical concepts behind Machine Learning
- Python and it for statistical analysis
- linear and logistic regressions, cluster and factor analysis
- Machine Learning algorithms application with Python, using NumPy, stats models and scikit-learn
- deep neural networks
- Improving performance with under fitting, over fitting, training, validation, n-fold cross validation, testing, and hyper-parameters concepts

**2. R Programming A-Z™: R For Data Science With Real Exercises!**

R is one of the most frequently used programming language for Data Science project development. This Udemy course is designed to help you out with understanding the basic concepts of R programming and its syntax. You will get to learn about all concepts of R programming through implementing them in small day to day problems. One will also learn to customize the R studio configuration as per the preferences of the user while working with different sorts of huge data sets.

**What you will Learn:**

- programming in R with all basic concepts
- using R Studio
- create vectors in R
- create variables
- Learn about integer, double, logical, character and other types in R
- loops in R
- matrices in R
- customizing R studio as per preferences
- Understanding Law of Large Numbers and Normal distribution
- working with statistical data in R, financial data in R, and sports data in R

**3. Statistics for Data Science and Business Analysis**

Statistics, probability, and other mathematical concepts play a great role in Data Analysis and predicting the accuracy and to design a suitable model to perform the necessarily required operations. It is important to have a clear statistic approach for data analysis for different business analysis. This Udemy course majorly covers all the mathematical and statistic algorithms using different data types measuring correlation and interdependence between data sets with hypothetical testing measures.

**What you will Learn:**

- Understanding the fundamentals of statistics
- Working with different types of data
- different types of data
- measures of central tendency, asymmetry, variability, correlation and covariance
- different types of distributions
- Estimate confidence intervals and hypothesis testing
- data driven decision making
- regression analysis and dummy variables

**4. Data Science: Supervised Machine Learning in Python**

Supervised Machine Learning is a part of Data Science. In this learning procedure, the machine maps the current input to an output based on example training input output pairs. Supervised learning is majorly used in business analysis or sales prediction models. In this Udemy course, you will learn about the different types of Supervised machine learning algorithms using Python as our programming language.

**What you will learn:**

- Implementation and limitations of K-Nearest Neighbors in Python
- solving binary and multi-class classification problems
- Naive Bayes and General Bayes Classifiers in Python
- Decision Tree in Python
- Perceptro in Python and its limitations
- cross validation and hyper parameters
- feature extraction and feature selection
- pros and cons of classic machine learning and deep learning
- Implementing machine learning on web service

**5. Data Science Career Guide – Interview Preparation**

An interview is the most important round which is needed to be qualified to get your dream job. This Udemy course is specifically designed for those who wish to get a job in the Data Science field. It consists of all beginner and advanced level interview questions which can be asked in an interview. It will help you refresh all your Data science basics with addition to some additional topics like Database and experiment-based questions. It will help you add experience to your resume and add up the strength to it.

**What you will learn:**

- Data Science questions
- probability and statistics questions.
- good experiment design.
- SQL practice questions.

**6. Complete Machine Learning and Data Science: Zero to Mastery**

Machine Learning and Data Science are closely related to each other. Machine Learning is the algorithmic science of making a computer perform certain tasks without being explicitly programmed for each one of them, whereas Data Science is an interlinked field that uses a scientific algorithm with mathematical progressions to classify, sort, and analyze structured or unstructured data. in this Udemy course, you will get to know about the modern tools for Data Science applications performing several different tasks using the conceptual knowledge of Machine Learning, Deep Learning, Data Analysis, etc. which are to be applied through python programming.

**What you will learn:**

- Data Science and Machine Learning
- Deep Learning, Transfer Learning and Neural Networks with Tensorflow
- Using modern tools like Google, Apple, Amazon and Facebook
- Problem classification and Implementing Machine Learning algorithms
- Python 3
- pre processing data, cleaning data, and analyzing large amounts of data.
- Developer Environment setup
- Supervised and Unsupervised Learning
- data visualization tools like Matplotlib and Seaborn
- Pandas, NumPy, Scikit-learn
- Hadoop, Spark and Kafka
- Classification and Regression modelling
- Transfer Learning

**7. Data Science:Deep Learning in Python**

Deep Learning is an sub component of Machine Learning. It makes the system learn with the help of previous examples. It is a procedure of high level feature extraction through the formation of multiple layers of the raw input. Feature extraction from an image by applying Image Processing techniques is one of the examples of Deep Learning, in Data Science Deep Learning plays an important role in extracting highly precise data through a data set with different structures. In this Udemy course, you are going to learn about different important Deep Learning concepts and how to implement the same.

**What you will learn:**

- neural network
- Installing TensorFlow
- neural network in Python
- numpy
- TensorFlow
- different types of neural networks
- back-propagation rule

**8. Data Science and Machine Learning Bootcamp with R**

Data Science and Machine Learning are companions, one will need the help of machine learning algorithms to perform the data science operations. In this Udemy course, one will learn R programming along with the basic concepts of both machine learning and data science such as data analysis, data visualization, web scraping, data manipulation along with many other machine learning algorithms used for data handling and manipulation.

**What you will learn:**

- Programming in R
- Data Analysis
- Data Visualizations
- handling csv,excel,SQL files or web scraping
- data manipulation
- Machine Learning Algorithms

**9. Machine Learning, Data Science and Deep Learning with Python**

Data Science, Machine Learning, and Deep Learning are everywhere, and python programming has always stood as their powerhouse. This Udemy course will introduce you to all the different aspects of Data Science, Machine Learning, and Deep Learning from their very basics. one will need to have a hands-on practice on Python programming to swim along this course, without it you won’t be able to understand the workflow of algorithms.

**What you will learn:**

- artificial neural networks building with Tensorflow and Keras
- Classify images, data, and sentiments
- Prediction making using linear regression, polynomial regression, and multivariate regression
- Data Visualization using MatPlotLib and Seaborn
- Apache Spark’s MLLib
- PAC-Man bot
- Classification with K-Means clustering, Support Vector Machines , KNN, Decision Trees, Naive Bayes,etc.
- train/test data and K-Fold cross validation
- evaluating A/B tests using T-Tests and P-Values

**10. Machine Learning A-Z™: Hands-On Python & R In Data Science**

This is not like all the normal Udemy courses or training programs; after its completion, you will realize what all in-hand problems it covered and how important they are to a data scientist. It covers one major part of Data Science that is machine learning with two popularly used programming languages Python and R. One of the strong positive aspects of this course is that it is covering the most useful data science concepts with practically possible solutions to them.

**What you will Learn:**

- Master Machine Learning on Python & R
- accurate predictions
- powerful analysis
- robust Machine Learning models
- Reinforcement Learning, NLP and Deep Learning

### Final Thoughts

Udemy has always been one of the finest and an extraordinary platform empowering and engaging millions of learners through its to the point content and a wide variety of options available for every stream.

Data Science is one of the most diverse streams of computer sciences, having a touch of several other streams like machine learning, deep learning, artificial intelligence, digital image processing, etc. and being used in every single one of them. if one aspires to be an extraordinary data scientist and excel overall then they should possess the knowledge of all those fields with a clear goal to acquire.