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Why Buy This Book?
3 Pack Bundle
What’s Covered?
What is Data Science? Why Learn Data Science? Basics of Data and Information Understanding Data Basic Math for Data Science Finding Patterns in Data Introduction to Python Writing Your First Code Working with Data in Python Overview of Exploratory Data Analysis (EDA) Creating Simple Plots Drawing Insights from Data Cleaning Messy Data Handling Missing Values Organizing Data for Analysis What is Machine Learning? Types of Machine Learning Training Your First Model Linear Regression Decision Trees Clustering Basics Feature Engineering Dimensionality Reduction Handling Time Series Data Making Your Model Accessible Deploying a Model in the Cloud Sharing Your Insights Introduction to Big Data Cloud Computing for Beginners Basics of Distributed Computing Ethical Considerations Bias in Data Science Transparency and Fairness Planning Your Project Executing the Project Presenting Your Findings
Overview of Excel interface Basics of navigating and working with sheets Introduction to cells, rows, columns, and ranges Understanding basic functions (SUM, AVERAGE, COUNT) Working with mathematical and statistical functions Introduction to text functions for data manipulation Working with logical functions (IF, AND, OR) Exploring lookup functions (VLOOKUP, HLOOKUP, INDEX, MATCH) Introduction to array formulas Identifying and handling missing data Removing duplicates and dealing with errors Text-to-columns and data splitting techniques Formatting data for analysis Creating basic charts and graphs Tips for effective data presentation Introduction to PivotTables for dynamic data analysis Creating PivotCharts for visual insights Customizing and formatting PivotTables and PivotCharts Time-saving shortcuts and productivity hacks Excel with AI Overview of SǪL and its applications Introduction to relational databases Basic SǪL syntax and structure Creating and modifying tables with CREATE and ALTER Understanding data types and constraints Basics of SELECT statements Filtering data with WHERE clause Sorting results with ORDER BY Understanding aggregate functions (SUM, AVG, COUNT) Grouping data with GROUP BY Working with complex WHERE conditions Using operators (AND, OR, NOT, etc) Introduction to window functions Performing analytic queries with OVER clause Performing INNER and OUTER joins Using subqueries for complex queries Understanding and using CASE statements in SǪL Applying CASE statements in data analysis scenarios Introduction to Common Table Expressions Using CTEs for recursive queries and data manipulation Optimization of queries Optimization of queries using AI Interview based SǪL queries Working on industry orient data Problem solving using SǪL on industrial data
Introduction to Machine Learning What is Machine Learning? Why Machine Learning Matters Types of Machine Learning Learning with Examples (Supervised) Discovering Patterns (Unsupervised) Learning from Experience (Reinforcement) Understanding Data What is Data in Machine Learning? Cleaning and Preparing Data Basic Algorithms Predicting with Linear Regression Making Decisions with Decision Trees Grouping with Clustering Improving Predictions Support Vector Machines Learning Nearest Neighbors The Power of Ensembles Combining Predictions Random Forests Introduction to Neural Networks What are Neural Networks? Training Neural Networks Working with Text (Natural Language Processing) Understanding and Analyzing Text Making Sense of Sentiments Making Sense of Images (Computer Vision) Identifying Objects Understanding Images Evaluating and Tuning Models How Well is Your Model Doing? Making Your Model Better Challenges and Considerations Being Ethical in Machine Learning What’s Next in Machine Learning?No Problem! Mail us at [email protected] to Connect
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