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Learning Data Science though is not an easy task. The field traverses through Computer Science, Programming, Information Theory, Statistics and Artificial Intelligence. College/University courses in this field are expensive. Becoming a Data Scientist through self-study is challenging since it requires going through multiple books, websites, searches and exercises and you will still end up feeling "not complete" at the end of it. So how do you acquire full-stack Data Science skills that will get you a and give you the confidence to execute it? Applied Data Science with Python addresses the problem. This course provides extensive, end-to-end coverage of all activities performed in a Data Science project. If teaches application of the latest techniques in data acquisition, transformation and predictive analytics to solve real world business problems. The goal of this course is to teach practice rather than theory. Rather than deep dive into formulae and derivations, it focuses on using existing libraries and tools to produce solutions. It also keeps things simple and easy to understand. Through this course, we strive to make you fully equipped to become a developer who can execute full fledged Data Science projects. By taking this course, you will Appreciate what Data Science really is Understand the Data Science Life Cycle Learn to use Python for executing Data Science Projects Master the application of Analytics and Machine Learning techniques Gain insight into how Data Science works through end-to-end use cases. By becoming a student of V2 Maestros, you will also get maximum discounts on all of our other current and future courses (coupon codes inside the course material). You will also get prompt support of all your queries and questions. We continuously strive to improve our course material to reflect the latest trends and technologies Category: Development / Programming Languages What are the requirements? Programming experience in Python Experience in analyzing Data preferred What am I going to get from this course? Over 43 lectures and 8.5 hours of content! Appreciate what Data Science really is Understand the Data Science Life Cycle Learn to use Python for executing Data Science Projects Master the application of Analytics and Machine Learning techniques What is the target audience? IT Professionals aspiring to be Data Scientists Students who want to learn about Data Science domain Statisticians and Project Managers who want to expand their horizon into Data Science SECTION 1: Introduction 1 About this course 10:48 Preview 2 About V2 Maestros 02:07 Preview 3 Resource Bundle Text SECTION 2: What is Data Science? 4 Basic Elements of Data Science 11:51 5 The Dataset 10:44 6 Learning from relationships 12:55 Preview 7 Modeling and Prediction 09:31 8 Use Cases for Data Science 07:47 SECTION 3: Data Science Life Cycle 9 Stage 1 - Setup 11:46 10 Stage 2 - Data Engineering 11:57 11 Stage 3 & 4 - Analysis and Production 19:16 SECTION 4: Statistics for Data Science 12 Types of Data 07:29 13 Summary Statistics 16:10 14 Statistical Distributions 19:05 15 Correlations 10:09 SECTION 5: Python for Data Science 16 Python libraries Overview 16:42 17 Examples 1 - Series and Data Frames 16:28 18 Examples 2 - Grouping and Graphics 08:53 SECTION 6: Data Engineering 19 Data Acquisition 16:01 20 Data Cleansing 10:50 21 Data Transformations 11:09 22 Text Preprocessing TF-IDF 14:53 23 Python examples for Data Engineering 09:11 SECTION 7: Machine Learning and Predictive Analysis 24 Types of Analytics 12:08 Preview 25 Types of Learning 17:16 26 Analyzing results and errors 13:46 27 Linear Regression 19:00 28 Python Use Case : Linear Regression 18:44 29 Decision Trees 10:42 30 Python Use Case : Decision Trees 15:21 31 Naive Bayes Classifier 19:21 32 Python Use Case : Naive Bayes 06:50 33 Random Forests 10:31 34 Python Use Case : Random Forests 12:17 35 K-Means Clustering 11:53 36 Python Use Case : K-Means Clustering 09:36 37 Association Rules Mining 11:31 38 Python Use Case : Association Rules Mining 07:29 SECTION 8: Advanced Topics 39 Artificial Neural Networks and Support Vector Machines 04:35 40 Bagging and Boosting 11:27 41 Dimensionality Reduction 07:28 42 Python Use Case : Advanced Methods 07:39 SECTION 9: Conclusion 43 Closing Remarks 04:02