998 Data Science courses

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This module aims to develop knowledge from research activities to gain an understanding of international trade using Marketing , Social Media and how AI plays a role in International Marketing

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python-experienced attendees who wish to be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to: Understand how data analysts and scientists gather and analyze data Perform data analysis and data wrangling using Python Combine, group, and aggregate data from multiple sources Create data visualizations with pandas, matplotlib, and seaborn Apply machine learning (ML) algorithms to identify patterns and make predictions Use Python data science libraries to analyze real-world datasets Use pandas to solve common data representation and analysis problems Build Python scripts, modules, and packages for reusable analysis code Perform efficient data analysis and manipulation tasks using pandas Apply pandas to different real-world domains with the help of step-by-step demonstrations Get accustomed to using pandas as an effective data exploration tool. Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Geared for data team members with incoming Python scripting experience, Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding lessons, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. Students will leave the course armed with the skills required to use pandas to ensure the veracity of their data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Introduction to Data Analysis Fundamentals of data analysis Statistical foundations Setting up a virtual environment Working with Pandas DataFrames Pandas data structures Bringing data into a pandas DataFrame Inspecting a DataFrame object Grabbing subsets of the data Adding and removing data Data Wrangling with Pandas What is data wrangling? Collecting temperature data Cleaning up the data Restructuring the data Handling duplicate, missing, or invalid data Aggregating Pandas DataFrames Database-style operations on DataFrames DataFrame operations Aggregations with pandas and numpy Time series Visualizing Data with Pandas and Matplotlib An introduction to matplotlib Plotting with pandas The pandas.plotting subpackage Plotting with Seaborn and Customization Techniques Utilizing seaborn for advanced plotting Formatting Customizing visualizations Financial Analysis - Bitcoin and the Stock Market Building a Python package Data extraction with pandas Exploratory data analysis Technical analysis of financial instruments Modeling performance Rule-Based Anomaly Detection Simulating login attempts Exploratory data analysis Rule-based anomaly detection Getting Started with Machine Learning in Python Learning the lingo Exploratory data analysis Preprocessing data Clustering Regression Classification Making Better Predictions - Optimizing Models Hyperparameter tuning with grid search Feature engineering Ensemble methods Inspecting classification prediction confidence Addressing class imbalance Regularization Machine Learning Anomaly Detection Exploring the data Unsupervised methods Supervised methods Online learning The Road Ahead Data resources Practicing working with data Python practice

This comprehensive course will help you learn the basics to advanced mechanisms of chatbot development using deep learning with Python. This course is a complete package for beginners to learn chatbot fundamentals with deep learning and its applications and build it from scratch using deep learning (RNNs) with Python.

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Curriculum of the Bundle Course 01: Machine Learning with Python Module 01: Introduction to Algorithms Module 02: Preprocessing Module 03: Regression Module 04: Classification Course 02: Data Science & Machine Learning with R Data Science and Machine Learning Course Intro Data Types and Structures in R Data Types and Structures in R Intermediate R Data Manipulation in R Data Visualization in R Creating Reports with R Markdown Building Webapps with R Shiny Introduction to Machine Learning Starting A Career in Data Science Course 03: Python Programming for Everybody Module 01 A Installing Python Documentation Command Line Variables Simple Python Syntax Keywords Import Module Module 02 Additional Topics If Elif Else Iterable For Loops Execute Exceptions Module 03 Data Types Number Types More Number Types Strings More Strings Files Lists Dictionaries Tuples Sets Module 04 Comprehensions Definitions Functions Default Arguments Doc Strings Variadic Functions Factorial Module 05 Function Objects Lambda Generators Closures Classes Object Initialization Class Static Members Classic Inheritance Data Hiding Course 04: Advanced Diploma in User Experience UI/UX Design UX/UI Course Introduction Introduction To The Web Industry Foundations of Graphic Design UX Design (User Experience Design) UI Design (User Interface Design) Optimization Starting a Career in UX/UI Design Course 05: Data Structures Complete Course Unit 01: Introduction Unit 02: Arrays Unit 03: Liked List Unit 04: Stack Unit 05: Queues Unit 06: Priority Queues (PQs) Unit 07: Union Find Unit 08: Binary Search Trees Unit 09: Fenwick Tree Unit 10: Hash Tables Unit 11: Suffix Array Unit 12: AVL Trees Unit 13: Indexed Priority Queue Unit 14: Sparse Tables Course 06: Data Science with Python Unit 01: Introduction to Python Data Science Unit 02: Data Cleaning Packages Unit 03: Data Visualization packages Course 07: Computer Science: Graph Theory Algorithms Module 00: Promo Module 01: Introduction Module 02: Common Problem Module 03: Depth First Search Module 04: Breadth First Search Module 05: Breadth First Search Shortest Path on a Grid Module 06: Trees Module 07: Topological Sort Module 08: Dijkstra Module 09: Bellman-Ford Algorithm Module 10: Floyd-Warshall Algorithm Module 11: Bridge and Algorithm Points Module 12: Tarjan Algorithm Module 13: Travelling Salesman Problem (TSP) Module 14: Eulerian Paths and Circuits Module 15: Prim's Minimum Spanning Tree Algorithm Module 16: Network Flow Course 08: Higher Order Functions in Python - Level 03 Module 01: Course Introduction Module 02: Simple Higher Order Functions Module 03: Sorting with Keys Module 04: Map Function Module 05: Filter Function Module 06: List Comprehension Alternative Module 07: Recursion Introduction Course 09: AWS Essentials Section 01: AWS Foundations and Services Section 02: AWS Security and Costs Course 10: Cloud Computing / CompTIA Cloud+ (CV0-002) Section 01: What You Need to Know Section 02: Introducing the Cloud Section 03: System Requirements for Cloud Deployments Section 04: Cloud Storage Section 05: Cloud Compute Section 06: Cloud Networking Section 07: Cloud Security Section 08: Migrating to the Cloud Section 09: Maintaining Cloud Solutions Section 10: Troubleshooting Cloud Solutions Course 11: Introduction to Data Analysis Module 01: Introduction Module 02: Agenda and Principles of Process Management Module 03: The Voice of the Process Module 04: Working as One Team for Improvement Module 05: Exercise: The Voice of the Customer Module 06: Tools for Data Analysis Module 07: The Pareto Chart Module 08: The Histogram Module 09: The Run Chart Module 10: Exercise: Presenting Performance Data Module 11: Understanding Variation Module 12: The Control Chart Module 13: Control Chart Example Module 14: Control Chart Special Cases Module 15: Interpreting the Control Chart Module 16: Control Chart Exercise Module 17: Strategies to Deal with Variation Module 18: Using Data to Drive Improvement Module 19: A Structure for Performance Measurement Module 20: Data Analysis Exercise Module 21: Course Project Module 22: Test your Understanding CPD 125 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Anyone from any background can enrol in this Machine Learning bundle. Persons with similar professions can also refresh or strengthen their skills by enrolling in this course. Students can take this course to gather professional knowledge besides their study or for the future. Requirements Our Machine Learning is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career path Having these various expertise will increase the value in your CV and open you up to multiple job sectors. Certificates Certificate of completion Digital certificate - Included

In the past, popular thought treated artificial intelligence (AI) as if it were the domain of science fiction or some far-flung future. In the last few years, however, AI has been given new life. The business world has especially given it renewed interest. However, AI is not just another technology or process for the business to consider - it is a truly disruptive force.

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This beginner's level course on Pandas acquaints you to the data analysis fundamentals using Pandas library and working with Pandas primary data structures, Series and DataFrame. Learn all about data input using indexing and filtering, data preprocessing, and handle missing values and duplicate rows to transform your data more efficiently with the help of this hands-on course.

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