Last updated 9/2018MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 3.12 GB | Duration: 9h 27m
Learn the fundamentals of data science and gain an in-depth understanding of data analysis with various Python packages What you'll learn Become proficient in working with real life data collected from different sources such as CSV files, websites, and databases Work with regression, classification, clustering, supervised and unsupervised machine learning, and much more! Understand -series decomposition, forecasting, clustering, and classification. Calculate the word frequencies using Data Science Techniques of Python. Carry out cluster analysis using visualization methods such as Dendrogram and Silhouette plots. Perform Cluster Analysis using Python Data Science Techniques Requirements Prior basic working knowledge of data analysis and Python will be useful. Description In today’s world, everyone wants to gain insights from the deluge of data coming their way. Data Science provides a way of finding these insights, and Python is one of the most popular languages for data mining, providing both power and flexibility in analysis. Thanks to its flexibility and vast popularity that data analysis, visualization, and Machine Learning can be easily carried out with Python.Starting out at the basic level, this Learning Path will take you through all the stages of data science in a step-by-step manner.This comprehensive 3-in-1 course is a comprehensive course packed with step-by-step instructions, working examples, and helpful advice on Data Science Techniques in Python. You’ll start off by creating effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience. You’ll learn how to develop statistical plots using Matplotlib and Seaborn to help you get insights into real size patterns hidden in data. Also explore useful libraries for visualization, Matplotlib and Seaborn, to get insights into data.By the end of this course, you’ll become an efficient data science practitioner by understanding Python's key concepts! Contents and OverviewThis training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Learning Python for Data Science, covers data analytics and machine learning using Python programming. In this course you’ll learn all the necessary libraries that make data analytics with Python. Learn the Numpy library used for numerical and scientific computation. Employ useful libraries for visualization, Matplotlib and Seaborn, to provide insights into data. 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. The second course, Python Data Science Essentials, covers fundamentals of data science with Python. This course takes you through all you need to know to succeed in data science using Python. Get insights into the core of Python data, including the latest versions of Jupyter Notebook, NumPy, Pandas and scikit-learn. Delve into building your essential Python 3.6 data science toolbox, using a single-source approach that will allow to work with Python 2.7 as well. Get to grips fast with data mug and preprocessing, and prepare for machine learning and visualization techniques.The third course, Practical Python Data Science Techniques, covers practical Techniques on Working with Data using Python. This video will b from exploring your data using the different methods like data acquisition, data cleaning, data mining, machine learning, and data visualization, applied to a variety of different data types like structured data or free-form text. Deal with data with a dimension and how to build a recommendation system as well as about supervised learning problems (regression and classification) and unsupervised learning problems (clustering). Perform text preprocessing steps that are necessary for every text analysis applications. Specifically, you’ll cover tokenization, stopword removal, stemming and other preprocessing techniques.By the end of the video course, you will become an expert in Data Science Techniques using Python.By the end of the course, you’ll learn the fundamentals of data science and gain an in-depth understanding of data analysis with various Python packages. About the AuthorsIlyas Ustun is a data scientist. He is passionate about creating data-driven analytical solutions that are of outstanding merit. Visualization is his favorite. After all, a picture is worth a thousand words. He has over 5 years of data analytics experience in various fields like transportation, vehicle re-identification, smartphone sensors, motion detection, and digital agriculture. His Ph.D. dissertation focused on developing robust machine learning models in detecting vehicle motion from smartphone accelerometer data (without using GPS). In his spare , he loves to swim and enjoy the nature. He loves gardening and his dream is to have a house with a small garden so he can fill it in with all kind of flowers.Luca Massaron is a data scientist and a marketing research director specialized in multivariate statistical analysis, machine learning and customer insight with over a decade of experience in solving real world problems and in generating value for stakeholders by applying reasoning, statistics, data mining and algorithms. From being a pioneer of Web audience analysis in Italy to achieving the rank of top ten Kaggler, he has always been passionate about everything regarding data and analysis and about demonstrating the potentiality of data-driven knowledge discovery to both experts and non-experts. Favouring simplicity over unnecessary sophistication, he believes that a lot can be achieved in data science just by doing the essential.Marco Bonzanini is a data scientist based in London, United Kingdom. He holds a Ph.D. in information retrieval from the Queen Mary University of London. He specializes in text analytics and search applications, and over the years, he has enjoyed working on a variety of information management and data science problems. He maintains a personal blog, where he discusses different technical topics, mainly around Python, text analytics, and data science. When not working on Python projects, he likes to engage with the community at PyData conferences and meetups, and he also enjoys brewing homemade beer. Overview Section 1: Learning Python for Data Science Lecture 1 The Course Overview Lecture 2 What Is Data Science? Lecture 3 Python Data Science Ecosystem Lecture 4 Installing Anaconda Lecture 5 Starting Jupyter Lecture 6 Basics of Jupyter Lecture 7 Markdown Syntax Lecture 8 1D Arrays with NumPy Lecture 9 2D Arrays with NumPy Lecture 10 Functions in NumPy Lecture 11 Random Numbers and Distributions in NumPy Lecture 12 Create DataFrames Lecture 13 Read in Data Files Lecture 14 Subsetting DataFrames Lecture 15 Boolean Indexing in DataFrames Lecture 16 Summarizing and Grouping Data Lecture 17 Matplotlib Introduction Lecture 18 Graphs with Matplotlib Lecture 19 Graphs with Seaborn Lecture 20 Graphs with Pandas Lecture 21 Machine Learning Lecture 22 Types of Machine Learning Lecture 23 Introduction to Scikit-learn Lecture 24 Linear Regression Lecture 25 Logistic Regression Lecture 26 K-Nearest Neighbors Lecture 27 Decision Trees Lecture 28 Random Forest Lecture 29 K-Means Clustering Lecture 30 Preparing Data for Machine Learning Lecture 31 Performance Metrics Lecture 32 Bias-Variance Tradeoff Lecture 33 Cross-Validation Lecture 34 Grid Search Lecture 35 Wrap Up Section 2: Python Data Science Essentials Lecture 36 The Course Overview Lecture 37 Introducing Data Science and Python Lecture 38 Getting Ready Lecture 39 A Glance at the Essential Packages Lecture 40 Introducing the Jupyter Notebook Lecture 41 Scikit-learn Toy Datasets Lecture 42 Data Loading and Preprocessing Lecture 43 Working with Categorical and Text Data Lecture 44 Creating NumPy Arrays Lecture 45 NumPy's Fast Operations and Computations Lecture 46 Introducing EDA Lecture 47 Building New Features Lecture 48 Dimensionality Reduction Lecture 49 The Detection and Treatment of Outliers Lecture 50 Validation Metrics Lecture 51 Testing and Validating Lecture 52 Cross-Validation Lecture 53 Hyperparameter Optimization Lecture 54 Feature Selection Lecture 55 Wrapping Everything in a Pipeline Lecture 56 Preparing Tools and Datasets Lecture 57 Linear and Logistic Regression Lecture 58 Naive Bayes Lecture 59 K-Nearest Neighbors Lecture 60 An Overview of Unsupervised Learning Section 3: Practical Python Data Science Techniques Lecture 61 The Course Overview Lecture 62 Loading Data into Python Lecture 63 A New Data Set – Exploratory Analysis Lecture 64 Getting Data in the Right Shape – Preprocessing and Cleaning Lecture 65 Tokenization – From Documents to Words Lecture 66 Stop-Words and Punctuation Removal Lecture 67 Text Normalization Lecture 68 Calculating Word Frequencies Lecture 69 Brief Overview of scikit-learn Lecture 70 Regression Analysis – Predicting a Quantity Lecture 71 Binary Classification – Predicting a Label (Out of Two) Lecture 72 Multi-Class Classification - Predicting a Label (Out of Many) Lecture 73 Cluster Analysis – Grouping Similar Items Lecture 74 Series Analysis with Pandas Lecture 75 Building a Movie Recommendation System Python programmer, aspiring data scientist who wants to learn the fundamentals of data science and gain an in-depth understanding of data analysis with Python. 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