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Learning Python for Data Science

Packt Admin
4 hrs

Learning Python for Data Science

Packt Admin
4 hrs
Included in GO1 PremiumStarting from $12 per user for teamsLearn moreTry for free
Included in GO1 PremiumStarting from $12 per user for teamsLearn moreTry for free

Course Overview 

Python is an open-source community-supported, general-purpose programming language that, over the years, has also become one of the bastions of data science. Thanks to its flexibility and vast popularity that data analysis, visualization, and machine learning can be easily carried out with Python. This course will help you learn the tools necessary to perform data science. In this course you will learn all the necessary libraries that make data analytics with Python a joy. You will get into hands-on data analysis and machine learning by coding in Python. You will also learn the Numpy library used for numerical and scientific computation. You will also employ useful libraries for visualization, Matplotlib and Seaborn, to provide insights into data. Further, you will learn various steps involved in building an end-to-end machine learning solution. The ease of use and efficiency of these tools will help you learn these topics very quickly. The video course is prepared with applications in mind. You will explore coding on real-life datasets, and implement your knowledge on projects. By the end of this course, you'll have embarked on a journey from data cleaning and preparation to creating summary tables, from visualization to machine learning and prediction. This video course will prepare you to the world of data science. Welcome to our journey! The code bundle for this video course is available at - https://github.com/PacktPublishing/Learning-Python-for-Data-Science. Style and Approach: This course consists of examples giving it a practical approach with a detailed explanation to the concepts. Lectures are followed by hands-on coding where youâll learn how to code in Python by using real-world datasets. This way, you will have the chance to repeat the process and compare your coding and results with the ones provided by the lecturer. This will enable you to practice the knowledge you've gained with each video.

Target Audience 

This course is an introductory-level data science course for aspiring data scientists with a basic understanding of coding in Python and little to no knowledge of data analytics. If you already know Python, or another programming language; if you want to apply your knowledge in computer programming to data analytics, and learn how to conduct data science; if you have used another language for data science such as R, and want to add Python to your skillset, then this course is for you. Knowledge of intro-level programming topics such as variables, if-else constructs, for and while loops, and functions are highly recommended but not required.

Section 1: Beginning the Data Science Journey
1.1 The Course Overviewvideo
1.2 What Is Data Science?video
1.3 Python Data Science Ecosystemvideo
Section 2: Introducing Jupyter
2.1 Installing Anacondavideo
2.2 Starting Jupytervideo
2.3 Basics of Jupytervideo
2.4 Markdown Syntaxvideo
Section 3: Understanding Numerical Operations with NumPy
3.1 1D Arrays with NumPyvideo
3.2 2D Arrays with NumPyvideo
3.3 Functions in NumPyvideo
3.4 Random Numbers and Distributions in NumPyvideo
Section 4: Data Preparation and Manipulation with Pandas
4.1 Create DataFramesvideo
4.2 Read in Data Filesvideo
4.3 Subsetting DataFramesvideo
4.4 Boolean Indexing in DataFramesvideo
4.5 Summarizing and Grouping Datavideo
Section 5: Visualizing Data with Matplotlib and Seaborn
5.1 Matplotlib Introductionvideo
5.2 Graphs with Matplotlibvideo
5.3 Graphs with Seabornvideo
5.4 Graphs with Pandasvideo
Section 6: Introduction to Machine Learning and Scikit-learn
6.1 Machine Learningvideo
6.2 Types of Machine Learningvideo
6.3 Introduction to Scikit-learnvideo
Section 7: Building Machine Learning Models with Scikit-learn
7.1 Linear Regressionvideo
7.2 Logistic Regressionvideo
7.3 K-Nearest Neighborsvideo
7.4 Decision Treesvideo
7.5 Random Forestvideo
7.6 K-Means Clusteringvideo
Section 8: Model Evaluation and Selection
8.1 Preparing Data for Machine Learningvideo
8.2 Performance Metricsvideo
8.3 Bias-Variance Tradeoffvideo
8.4 Cross-Validationvideo
8.5 Grid Searchvideo
8.6 Wrap Upvideo