Why Python for Data Science?
The programming requirements of data science demand a very versatile yet flexible language which is simple to write the code but can handle highly complex mathematical processing. Python is the well-established programming language for general computing as well as scientific computing. Moreover, it is being continuously upgraded in the form of a new addition to its plethora of libraries aimed at different programming requirements. Below we will discuss such features of python which makes it the preferred language for data science.
- It is simple and easy to learn, we can achieve results in fewer lines of code.
- Its simplicity also makes it robust to handle complex scenarios with minimal code and much less confusion on the general flow of the program.
- It supports cross-platform, thus the same code works with multiple environments.
- It executes faster than other similar languages used for data analysis like R and MATLAB.
- Its excellent memory management capability, especially garbage collection makes it versatile in gracefully managing a very large volume of data transformation.
- Python has got a very large collection of libraries and packages, which serve as special-purpose analysis tools where we can directly use code from other languages (Java or C).
In Python for Data Science & Machin learning Bootcamp
- This is a comprehensive course brought to you by Udemy, which will guide you to learn how to use the power of Python for data analysis, create beautiful visualizations, and use powerful machine learning algorithms!
- Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!
- This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!
- This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture, this is one of the most comprehensive courses for data science and machine learning on Udemy!
Who this course is for?
- The python for data science and ML course is meant for people with at least some programming experience
The course on python for data science and machine learning Bootcamp will teach you how to program, how to perform data analysis, create amazing visualizations, and how to use Machine Learning with Python!
1. Programming with Python
- Introduction to the course.
- Learn how to install Python and Anaconda on your system.
- Learn about the Jupyter Notebooks system and optional virtual environments.
- Also, there will be a crash course in four parts providing overview and exercise related to the Python
2. Python for Data Analysis using NumPy
- Introduction to NumPy and NumPy arrays.
- A quick note on Array Indexing.
- NumPy operations and exercise.
3. Python for Data Analysis using Pandas
- Introduction to Pandas
- There will be a series of lectures on DataFrames in three parts
- About missing data
- Groupby function, merging, joining and concatenating
- Operations, data input, and output.
- Exercise and overview of the use of Pandas for Salary and E-commerce purchases.
4. Python for Data Visualization
- Introduction to use of Matplot library (Matplotlib), exercise, and overview.
- Introduction to Seaborn: Distribution plots, categorical plots and matrix plots, grids, regression plots, style & color. Exercise and overview of Seaborn
- Pandas built-in data visualization, Ploty and Cufflinks, and Geographical plotting
5. Data Capstone project
- Introduction and overview of the data capstone project
- 911 calls and Bank data (Finance data) projects overview and solutions.
6. Introduction to Machine learning
- Overview of Supervised learning, evaluating performance (classification and Regression error metrics)
- Machine learning with python
7. Machine Learning Algorithms
- Linear regression (model selection and updates for SciKit Learn), linear regression with python.
- Cross-validation and bias-variance trade-off.
- Logistic regression with Python.
- Introduction to various classifiers and exercise with Python on K-Nearest Neighbors (KNN), Decision Trees, Random Forests, Support Vector Machines (SVM), K-Means Clustering.
- Principal component analysis with Python.
- Recommender Systems with Python
8. Natural Language Processing with python.
9. Neural Networks and Deep Learning
- Introduction to Artificial Neural Networks (ANN).
- Downloading and installing TensorFlow notebooks.
- Perceptron model, Neural networks, Activation functions, Multi-class classification considerations.
- Cost functions and Gradient descent, Backpropagation, Tensorflow Vs Keras.
Regression code along with exploratory data analysis, data preprocessing and creating a model, model evaluation, and predictions.
Classification code along with model evaluation and predictions, EDA, and preprocessing, dealing with overfitting and evaluation.
Project solutions dealing with missing data and categorical data, data preprocessing, creating and training a model and model evaluation, Tensor board.
10. Big Data and Spark with Python
- Big data and Spark overview and local Spark setup.
- Learn about Big Data, Spark, and how to use it with Python with Amazon Web Services.
- In detail on AWS account setup & a quick note on AWS security.
- EC2 instance setup on AWS, for Windows user.
- EC2 instance setup for Mac/Linux users using SSH.
- Step by Step instructions for setting up Hadoop, Spark, and Jupyter Notebook on your EC2 Ubuntu Instance.
- Lambda Expression review: Learn about Big Data, Spark, and how to use it with Python with Amazon Web Services!
If you have already done the python for data science and machine learning bootcamp, kindly post your review in our reviews section. It would help others to get useful information and better insight of the course offered.
- Online Course
- 1-4 Weeks
- Paid Course (Paid certificate)
- Data Science with 'Python' Deep learning Keras Machine learning Natural language processing Spark TensorFlow