Data Science, Analytics, and AI for Business and the Real World™

Right now, despite the Covid-19 economic contraction, traditional businesses are hiring data scientists in droves! Therefore, data scientist has become the top job in the U.S. for the last four years running.

However, data science has a difficult learning curve. This course seeks to fill all those gaps and has a comprehensive syllabus that tackles all the major components of data science knowledge.

You will be using data science to solve common business problems throughout this course. You will start with the basics of Python, Pandas, Scikit-learn, NumPy, Keras, Prophet, statsmod, SciPy, and more. You will learn statistics and probability for data science in detail. Then, you will learn visualization theory for data science and analytics using Seaborn, Matplotlib, and Plotly.

You will look at dashboard design using Google Data Studio along with machine learning and deep learning theory/tools.

Then, you will be solving problems using predictive modeling, classification, and deep learning. After this, you will move your focus to data analysis and statistical case studies, data science in marketing, and data science in retail.

Finally, you will see deployment to the cloud using Heroku to build a machine learning API.

By the end of this course, you will learn all the major components of data science and gain the confidence to enter the world of data science.

All the code files and the resource files are uploaded on the GitHub repository at https://github.com/PacktPublishing/Data-Science-Analytics-AI-for-Busine…-

Type
video
Category
Data
publication date
2022-03-21
what you will learn

Look at machine learning algorithms with Scikit-learn
Create beautiful charts, graphs, and visualizations that tell a story with data
Understand common business problems and how to apply data science
Create data dashboards with Google Data Studio
Learn to apply data science in marketing and retail
Integrate big data analysis and machine learning with PySpark

duration
1850
key features
Explore 16 statistical and data analysis, and six predictive modeling and classifiers case studies * Work on four: data science in marketing and retail, and two time-series forecasting case studies * Dive into three Natural Language Processing and one PySpark big data case studies, and a deployment project
approach
This course has a comprehensive syllabus that tackles all the major components of data science. The course content is over 30 hours long and it also comes bundled with real-life 35+ practical case studies.

This is a highly practical course, and all programming is taught from scratch, making it beginner-friendly.
audience
This course is designed for beginners in data science; business analysts who wish to do more with their data; college graduates who lack real-world experience; business-oriented persons who would like to use data to enhance their business; software developers or engineers who would like to start learning data science. Anyone looking to become more employable as a data scientist and with an interest in using data to solve real-world problems will enjoy this course thoroughly.

No need to be a programming or math whiz; basic high school math will be sufficient.
meta description
Learn to use data science and statistics to solve business problems and gain insights into everyday problems with 35+ case studies
short description
This course focuses on understanding all the basic theory and programming skills required as a data scientist, featuring 35+ practical case studies covering common business problems faced by them.

This course seeks to fill all those gaps in knowledge that scare off beginners and simultaneously apply your knowledge of data science and deep learning to real-world business problems.
subtitle
Use data science and statistics to solve and gain insights into real-world problems with 35+ case studies
keywords
Data Science, Analytics, AI, Python, Pandas, Scikit-learn, NumPy, Keras, Statsmod, SciPy, Statistics for Data Science, Visualization, Seaborn, Matplotlib and Plotly, Dashboard Design using Google Data Studio, Machine Learning Theory, Deep Learning
Product ISBN
9781803240848