The Comprehensive Data Analyst Course – Download Free

Free Download What you'll learn
Basics of Python
Introduction to Numpy package for handling arrays
Introduction to Pandas package for cleaning and analysing data
Introduction to SQL
Basics of Linear Algebra - What is a point, Line, Distance of a point from a line
What is a Vector and Vector Operations
What is a Matrix and Matrix Operations
Visualizing data, including bar graphs, pie charts, histograms
Data distributions, including mean, variance, and standard deviation, and normal distributions and z-scores
Analyzing data, including mean, median, and mode, plus range and IQR and box plots
Data Distributions like Normal and Chi Square
Probability, including union vs. intersection and independent and dependent events and Bayes' theorem
Central Limit Theorem
Hypothesis Testing
Requirements
Foundational Mathematics
Description
THE COMPREHENSIVE DATA ANALYST COURSE IS SET UP TO MAKE LEARNING FUN AND EASYThis 100+ lesson course includes 20+ hours of high-quality video and text explanations of everything from Linear Algebra, Probability, Statistics, Permutation and Combination. Topic is organized into the following sections:Python Basics, Data Structures - List, Tuple, Set, Dictionary, StringsPandas and NumpyLinear Algebra - Understanding what is a point and equation of a line. What is a Vector and Vector operationsWhat is a Matrix and Matrix operationsData Type - Random variable, discrete, continuous, categorical, numerical, nominal, ordinal, qualitative and quantitative data typesVisualizing data, including bar graphs, pie charts, histograms, and box plotsAnalyzing data, including mean, median, and mode, IQR and box-and-whisker plotsData distributions, including standard deviation, variance, coefficient of variation, Covariance and Normal distributions and z-scores.Different types of distributions - Uniform, Log Normal, Pareto, Normal, Binomial, BernoulliChi Square distribution and Goodness of FitCentral Limit TheoremHypothesis TestingProbability, including union vs. intersection and independent and dependent events and Bayes' theorem, Total Law of ProbabilityHypothesis testing, including inferential statistics, significance levels, test statistics, and p-values.Permutation with examplesCombination with examplesExpected ValueDonors Choose case studyAND HERE'S WHAT YOU GET INSIDE OF EVERY SECTION:We will start with basics and understand the intuition behind each topic.Video lecture explaining the concept with many real-life examples so that the concept is drilled in.Walkthrough of worked out examples to see different ways of asking question and solving them.Logically connected concepts which slowly builds up. Enroll today! Can't wait to see you guys on the other side and go through this carefully crafted course which will be fun and easy.YOU'LL ALSO GET:Lifetime access to the courseFriendly support in the Q&A sectionUdemy Certificate of Completion available for download30-day money back guarantee
Overview
Section 1: Basic Python for Data Analysis
Lecture 1 Keywords, Identifiers and Variables
Lecture 2 Variable Assignment
Lecture 3 Strings & List
Lecture 4 Tuple
Lecture 5 Set
Lecture 6 Dictionary
Lecture 7 Data type conversion
Lecture 8 Python Comments
Lecture 9 Print Statement
Lecture 10 Python Arithmetic and Logical Operators
Lecture 11 Identity & Membership Operators
Lecture 12 For & While loop
Lecture 13 Conditional Statement
Lecture 14 Functions
Lecture 15 Modules
Lecture 16 List - Part 1
Lecture 17 List - Part 2
Lecture 18 List - Part 3
Lecture 19 List - Part 4
Lecture 20 List - Part 5
Lecture 21 Tuple - Part 1
Lecture 22 Tuple - Part 2
Lecture 23 Set - Part 1
Lecture 24 Set - Part 2
Lecture 25 Set - Part 3
Lecture 26 Dictionary
Lecture 27 Strings
Lecture 28 Numpy Introduction
Lecture 29 Creating arrays
Lecture 30 Array Operations - Part 1
Lecture 31 Array Masking
Lecture 32 Array Operations - Part 2
Lecture 33 Array Operations - Part 3
Lecture 34 Array broadcasting
Lecture 35 Array - Shape Manipulation & Sorting
Section 2: Basics of SQL
Lecture 36 SQL Introduction
Lecture 37 Select Command
Lecture 38 Limit Command
Lecture 39 Column Filtering
Lecture 40 DISTINCT command
Lecture 41 WHERE command
Lecture 42 AGGREGATE Functions
Lecture 43 GROUP BY command
Lecture 44 AND, OR, NULL commands
Lecture 45 LIKE command & WILDCARD characters
Lecture 46 JOINS - Part 1
Lecture 47 JOINS - Part 2
Lecture 48 JOINS - Part 3
Lecture 49 IN command
Lecture 50 HAVING Command
Lecture 51 UNION command
Lecture 52 ANY & ALL command
Section 3: Principal Component Analysis
Lecture 53 Preface for Dimensionality Reduction - Part 1
Lecture 54 Preface for Dimensionality Reduction - Part 2
Lecture 55 Preface for Dimensionality Reduction - Part 3
Lecture 56 Preface for Dimensionality Reduction - Part 4
Lecture 57 Preface for Dimensionality Reduction - Part 5
Lecture 58 Gometric Intuition of PCA
Lecture 59 Mathematical formulation of PCA - Part 1
Lecture 60 Mathematical formulation of PCA - Part 2
Lecture 61 Mathematical formulation of PCA - Part 3
Lecture 62 Failure cases of PCA
Lecture 63 Connecting Colab to Gdrive
Lecture 64 Understanding MNIST dataset
Lecture 65 Visualizing MNIST single digit
Lecture 66 MNIST Visualization - Method 1
Lecture 67 MNIST Visualization - Method 2
Aspiring Data Analysts,Business Analyst,Business Managers,Anyone wanting to learn basics of story telling through data

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