Data Analytics Career Track

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[img]/storage-10/0424/yO5GvDxBAiJ0TRYDMUiYyuaISmkzm5I4.jpg[/img]
Published 4/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 10.39 GB | Duration: 27h 14m

Learn the Best Utilization of Excel, SQL, and Python for A-Z Data Analysis and Become a Successful Data Analyst in 2024.
[b]What you'll learn[/b]
You will gain proficiency in Excel, SQL, and Python for data analysis. Prepare for a career as a data analyst with essential professional skills and knowledge.
You will work on practical data analysis projects to apply learned skills. Enhance problem-solving abilities through hands-on data analysis exercises.
You will learn facts and theories for data analysis, statistical analysis, hypothesis testing, and machine learning for foundations of data analytics.
You will learn A-Z data cleaning and manipulation methods, sorting, sorting and conditional filtering, formulas, and functions, graphs and charts in Excel.
You will learn advanced analysis in PIVOT tables and charts, Data Analysis ToolPak for statistical analysis and interactive dashboard in Excel.
You will learn RDBMS fundamentals, covering key concepts such as primary and foreign keys, data types, and the various types of RDBMS and more.
You will learn full stack manipulation of tables, columns, constraints, indices, null values, filtering, joining methods in MySQL or structured query language.
You will learn the important Python programming basics such as variables naming, data types, lists, dictionaries, dataframes, sets, loops, functions etc.
You will master a range of methods and techniques for data cleaning, sorting, filtering, data manipulation, transformation, and data preprocessing in Python.
You will learn to use Python for data visualizations, exploratory data analysis, statistical analysis, hypothesis testing methods and machine learning models.
You will pass 50+ practical assignments, 140+ coding exercises, 10 quizzes with 100+ questions, on all the topics over the entire career track.
You will accomplish two capstone projects on Bank data analysis and Sport data analysis at the end to get the full view of data analysis workflow.
[b]Requirements[/b]
Access to computer and internet
Basic computer literacy
No coding experience required
Dedication, patience and perseverance
[b]Description[/b]
Are you eager to embark on a rewarding journey into the world of data analytics? Welcome to the Data Analytics Career Track, where you'll gain a comprehensive skill set and invaluable knowledge to thrive as a data analyst.Course Overview: In this meticulously crafted course, you'll delve into the core tools and techniques of data analysis: Excel, SQL, and Python. From foundational concepts to advanced methodologies, each module is designed to equip you with the expertise needed to excel in the dynamic field of data analytics.Key Objectives:Proficiency in Essential Tools: Master Excel, SQL, and Python for data analysis, providing you with a versatile toolkit for tackling real-world challenges.Hands-on Experience: Engage in practical data analysis projects and coding exercises, honing your problem-solving skills through immersive learning experiences.Foundational Knowledge: Gain insights into data analysis theories, statistical methods, hypothesis testing, and machine learning fundamentals, laying a solid groundwork for your career.Data Manipulation Mastery: Learn A-Z data cleaning and manipulation techniques, including sorting, filtering, conditional formatting, and advanced analysis with pivot tables and charts.Database Fundamentals: Acquire a deep understanding of relational database management systems (RDBMS), covering key concepts such as primary keys, foreign keys, and SQL manipulation.Python Proficiency: Explore Python programming basics and advanced data analysis techniques, including data visualization, exploratory data analysis, and machine learning model implementation.Practical Assignments: Challenge yourself with over 50 practical assignments, 140 coding exercises, and 10 quizzes spanning the breadth of the course curriculum.Capstone Projects: Apply your newfound skills to real-world scenarios with two comprehensive capstone projects focused on bank data analysis and sports data analysis, providing a holistic view of the data analytics workflow.Benefits of the Course:Career Readiness: Prepare for a successful career as a data analyst with essential professional skills and practical knowledge.Versatility: Gain proficiency in multiple tools and techniques, making you adaptable to diverse data analysis scenarios and industry demands.Problem-solving Skills: Enhance your analytical and critical thinking abilities through hands-on data analysis exercises and coding challenges.Industry-Relevant Learning: Stay ahead of the curve with up-to-date insights into data analysis methodologies and best practices.Portfolio Enhancement: Build a robust portfolio showcasing your expertise through practical projects and assignments, demonstrating your readiness for the job market.Join us on the Data Analytics Career Track and unlock endless possibilities in the world of data analysis. Whether you're a seasoned professional or a novice enthusiast, this course is your gateway to a fulfilling and prosperous career in data analytics. Enroll today and embark on your journey to success!
[b]Overview[/b]
Section 1: Phase 1 - Data Analytics Fundamentals
Lecture 1 My instructions for this phase
Lecture 2 Extra note on analytical world of data
Section 2: All You Need to Know about Data Analysis
Lecture 3 Data analysis definition, types and examples
Lecture 4 Key components of data analysis
Lecture 5 Tools and technologies for data analysis
Lecture 6 Real-world application of data analysis
Section 3: Data Collection: Methods and Considerations
Lecture 7 Various sources of collecting data
Lecture 8 Population v/s sample and its methods
Lecture 9 Consideration for effective data collection
Section 4: Understand Data Cleaning and Its Methods
Lecture 10 Why you cannot ignore cleaning your data
Lecture 11 Various aspects of data cleaning
Lecture 12 Consideration for effective data cleaning
Section 5: Explore Joining and Concatenating Methods
Lecture 13 Various aspects of Joining datasets
Lecture 14 Adding extra data with concatenation
Section 6: Complete Picture of Exploratory Data Analysis
Lecture 15 EDA for generating significant insights
Lecture 16 Methods of exploratory data analysis Part 1
Lecture 17 Methods of exploratory data analysis Part 2
Lecture 18 Methods of exploratory data analysis Part 3
Lecture 19 Consideration for effective EDA
Section 7: Everything about Statistical Data Analysis
Lecture 20 The application of statistical test
Lecture 21 Types of statistical data analysis
Lecture 22 Statistical test v/s Exploratory data analysis
Lecture 23 A Recap on descriptive statistics methods
Lecture 24 Inferential statistics Part 1 - T-tests and ANOVA
Lecture 25 Inferential statistics Part 2 - Relationships measures
Lecture 26 Inferential statistics Part 3 - Linear regression
Lecture 27 Consideration for effective statistical analysis
Section 8: Concepts of Probabilities in Data Analysis
Lecture 28 Probability in data analysis
Lecture 29 Classical probability
Lecture 30 Empirical probability
Lecture 31 Conditional probability
Lecture 32 Joint probability
Section 9: Hypothesis Testing in Statistical Analysis
Lecture 33 Hypothesis testing for inferential statistics
Lecture 34 Selecting statistical test and assumption testing
Lecture 35 Confidence level, significance level, p-value
Lecture 36 Making decision and conclusion on findings
Lecture 37 Complete statistical analysis and hypothesis testing
Section 10: Explore Data Transformation and Its Methods
Lecture 38 Transforming data for improved analysis
Lecture 39 Techniques for data transformation Part 1
Lecture 40 Techniques for data transformation Part 2
Lecture 41 Consideration for effective data transformation
Section 11: Machine Learning for Predictive Efficiency
Lecture 42 ML for data analysis and decision-making
Lecture 43 Widely used ML methods in the data analytics
Lecture 44 Steps in developing machine learning model
Section 12: Explore Data Visualizations and Its Methods
Lecture 45 Visualizing data for the best insight delivery
Lecture 46 Several methods of data visualization Part 1
Lecture 47 Several methods of data visualization Part 2
Lecture 48 Several methods of data visualization Part 3
Lecture 49 Considerations for effective data visualization
Section 13: Phase 2 - Data Analytics in Microsoft Excel
Lecture 50 My instructions for this phase
Lecture 51 Extra note on functions and shortcuts
Section 14: Excel - Data Cleaning and Formatting
Lecture 52 Identifying and removing duplicates
Lecture 53 Dealing with missing values
Lecture 54 Dealing with outliers
Lecture 55 Finding and imputing inconsistent values
Lecture 56 Text-to-columns for data separation
Section 15: Excel - Data Sorting and Filtering
Lecture 57 Applying sorts & filters to narrow down data
Lecture 58 Advanced filtering with custom criteria
Section 16: Excel - Apply Conditional Formatting
Lecture 59 Highlighting cells based on criteria
Lecture 60 Findings top and bottom insights
Lecture 61 Creating color scales and color bars
Section 17: Excel - Formulas and Functions for Data Analysis
Lecture 62 SUM, AVERAGE, MIN, and MAX functions
Lecture 63 SUMIF, and AVERAGEIF functions
Lecture 64 COUNT, COUNTA, and COUNTIF functions
Lecture 65 YEAR, MONTH and DAY for date manipulation
Lecture 66 IF STATEMENTs for conditional operation
Lecture 67 VLOOKUP for column-wise insight search
Lecture 68 HLOOKUP for row-wise insight search
Lecture 69 XLOOKUP for robust & complex insight search
Section 18: Excel - Graphs and Charts for Data Visualization
Lecture 70 Analyze data with Stacked and cluster bar charts
Lecture 71 Analyze data with Pie chart and line chart
Lecture 72 Analyze data with Area chart and TreeMap
Lecture 73 Analyze data with Boxplot and Histogram
Lecture 74 Analyze data with Scatter plot and Combo chart
Lecture 75 Adjusting and decorating graphs and charts
Section 19: Excel - Data Analysis in PivotTables and PivotCharts
Lecture 76 PivotTables for GROUP data analysis PART 1
Lecture 77 PivotTables for CROSSTAB data analysis PART 2
Lecture 78 PivotCharts and Slicers for interactivity
Section 20: Excel - Data Analysis ToolPack for Statistical Analysis
Lecture 79 Descriptive statistics and analysis
Lecture 80 Independent sample t-test for two samples
Lecture 81 Paired sample t-test for two samples
Lecture 82 Analysis of variance - One way ANOVA
Lecture 83 Correlation analysis for relationship
Lecture 84 Multiple linear regression analysis
Section 21: Excel - Creating Interactive Dashboard
Lecture 85 Accumulating relevant information
Lecture 86 Creating a canvas for dashboard
Lecture 87 Developing the complete dashboard
Lecture 88 Final touch up for dashboard decoration
Section 22: Excel Project - Bank Churn Data Analysis
Section 23: Phase 3 - Database Management in MySQL
Lecture 89 My instructions for this phase
Lecture 90 Extra note on functions of MySQL
Section 24: Necessary Fundamentals of RDBMS
Lecture 91 RDBMS: example and importance
Lecture 92 Key features of RDBMS
Lecture 93 Primary key v/s Foreign key
Lecture 94 Types of relationship in RDBMS
Lecture 95 Data types in RDBMS
Section 25: Introduction to SQL for RDBMS
Lecture 96 Introduction to SQL language
Lecture 97 Various platforms of SQL
Section 26: Installing & Loading data in MySQL Interface
Lecture 98 Installing MySQL in Windows and Mac
Lecture 99 Loading CSV dataset in MySQL
Section 27: SQL - Getting Started: Database Management
Lecture 100 Creating database
Lecture 101 Selecting database
Lecture 102 Modifying database
Lecture 103 Deleting database
Lecture 104 SQL query for database management
Section 28: SQL - Fundamental Queries in SQL
Lecture 105 SELECT....FROM: select data from table
Lecture 106 DISTINCT: selecting unique values for column
Lecture 107 AS: selecting columns based on aliases
Lecture 108 WHERE: selecting data based on condition
Lecture 109 Basic SQL Queries
Section 29: SQL - Managing Tables in Database System
Lecture 110 CREATE: creating table
Lecture 111 NOT NULL: limiting null values
Lecture 112 UNIQUE: limiting duplicates
Lecture 113 INSERT INTO: adding values in columns
Lecture 114 UPDATE: updating values based on condition
Lecture 115 DELETE: deleting values based on condition
Lecture 116 TRUNCATE: deleting all the values except table
Lecture 117 DROP: removing entire table
Lecture 118 CHECK: limiting specific values in columns
Lecture 119 Managing Tables in SQL
Section 30: SQL - Working with Columns and Constraint
Lecture 120 ADD COLUMN: adding new column
Lecture 121 MODIFY COLUMN: replacing data types
Lecture 122 RENAME COLUMN: changing column names
Lecture 123 DROP COLUMN: deleting columns
Lecture 124 ADD CONSTRAINT: adding primary key
Lecture 125 ADD CONSTRAINT..REFERENCES: adding foreign key
Lecture 126 DROP CONSTRAINT: deleting keys
Lecture 127 Working with Columns and Constraint
Section 31: SQL - Working with Indexing Operation
Lecture 128 CREATE INDEX: creating new index
Lecture 129 CREATE UNIQUE INDEX: creating index without duplicates
Lecture 130 DROP INDEX: deleting existing index
Lecture 131 Working with Indexing Operation
Section 32: SQL - Dealing with NULL/MISSING values
Lecture 132 IS NULL: filtering the actual values out
Lecture 133 IS NOT NULL: filtering the missing values out
Lecture 134 Dealing with NULL values
Section 33: SQL - Various Aspects of Filtering Data
Lecture 135 AND: combining two or more conditions
Lecture 136 OR: flexible logical operator
Lecture 137 NOT: excluding values from filteration
Lecture 138 BETWEEN...AND: filtering ranges of values
Lecture 139 LIKE: filtering based on pattern
Lecture 140 IN: precise logic for multiple conditions
Lecture 141 LIMIT: filtering with limited data
Lecture 142 Various Aspects of Filtering Data
Section 34: SQL - IMPORTANT MySQL String Functions
Lecture 143 CHAR_LENGTH: finding the length of text
Lecture 144 CONCAT: adding different strings together
Lecture 145 LOWER: converting into lowercase
Lecture 146 UPPER: converting into uppercase
Lecture 147 TRIM: removing unnecessary gaps
Lecture 148 REPLACE: replacing old value by new value
Lecture 149 IMPORTANT MySQL String Functions
Section 35: SQL - IMPORTANT MySQL Arithmetic Functions
Lecture 150 ABS: negative to positive value
Lecture 151 SUM: calculating the total value
Lecture 152 AVG: calculating the average value
Lecture 153 COUNT: counting total items
Lecture 154 DIV: dividing numeric data
Lecture 155 MIN: finding the lowest value
Lecture 156 MAX: finding the highest value
Lecture 157 MySQL Arithmetic Functions
Section 36: SQL - IMPORTANT MySQL Transformation Functions
Lecture 158 POWER: multiple multiplications
Lecture 159 ROUND: decreasing the decimals
Lecture 160 SQRT and LOG: transformation functions
Lecture 161 MySQL Transformation Functions
Section 37: SQL - IMPORTANT MySQL Datetime Functions
Lecture 162 DATEFORMAT: formatting the date shape
Lecture 163 DATEDIFF: finding the date difference
Lecture 164 DAY/MONTH/YEAR: extracting parts of dates
Lecture 165 MySQL Datetime Functions
Section 38: SQL - Grouping and Sorting data in SQL
Lecture 166 ORDER BY: sorting data based on a column
Lecture 167 GROUP BY: group data analysis with functions
Lecture 168 Grouping and Sorting data
Section 39: SQL - JOINS for Data Retrievals in SQL
Lecture 169 INNER JOIN: joining on common values
Lecture 170 LEFT JOIN: joining on left table values
Lecture 171 RIGHT JOIN: joining on right table values
Lecture 172 CROSS JOIN: joining all values from tables
Lecture 173 JOINS for Data Retrievals
Section 40: SQL - Advanced Functions and Operations
Lecture 174 HAVING: advanced conditional format
Lecture 175 EXISTS: nested filtering between tables
Lecture 176 ANY: nested filtering between tables
Lecture 177 CASE: finding the conditional outcomes
Lecture 178 Advanced Functions and Operations
Section 41: SQL - Stored Procedure and Comments
Lecture 179 SQL comments systems
Lecture 180 Storing and executing procedures
Lecture 181 Stored Procedure and Comments
Section 42: Phase 4 - Data Analytics A-Z in Python
Lecture 182 My instructions for this phase
Lecture 183 Extra note on python data analysis
Lecture 184 Resources used in the course
Section 43: Setting Up Python and Jupyter Notebook
Lecture 185 Installing Python and Jupyter Notebook - Mac
Lecture 186 Installing Python and Jupyter Notebook - Windows
Lecture 187 More alternative methods - Check the article
Section 44: Python - Starting with Variables to Data Types
Lecture 188 Getting started with first python code
Lecture 189 Assigning variable names correctly
Lecture 190 Various data types and data structures
Lecture 191 Converting and casting data types
Lecture 192 Starting with Variables to Data Types
Section 45: Python - Operators in Python Programming
Lecture 193 Arithmetic operators (+, -, *, /, %, **)
Lecture 194 Comparison operators (>, <, >=, <=, ==, !=)
Lecture 195 Logical operators (and, or, not)
Lecture 196 Operators in Python Programming
Section 46: Python - Dealing with Data Structures
Lecture 197 Lists: creation, indexing, slicing, modifying
Lecture 198 Sets: unique elements, operations
Lecture 199 Dictionaries: key-value pairs, methods
Lecture 200 Several data structures
Section 47: Python - Conditionals Looping and Functions
Lecture 201 Conditional statements (if, elif, else)
Lecture 202 Nested logical expressions in conditions
Lecture 203 Looping structures (for loops, while loops)
Lecture 204 Defining, creating, and calling functions
Lecture 205 Conditionals Looping and Functions
Section 48: Python - Sequential Cleaning and Modifying Data
Lecture 206 Preparing notebook and loading data
Lecture 207 Identifying missing or null values
Lecture 208 Method of missing value imputation
Lecture 209 Exploring data types in a dataframe
Lecture 210 Dealing with inconsistent values
Lecture 211 Assigning correct data types
Lecture 212 Dealing with duplicated values
Lecture 213 Sequential data cleaning and modifying
Section 49: Python - Various Methods of Data Manipulation
Lecture 214 Sorting data by column and order
Lecture 215 Filtering data with boolean indexing
Lecture 216 Query method for precise filtering
Lecture 217 Filtering data with isin method
Lecture 218 Slicing dataframe with loc and iloc
Lecture 219 Filtering data for many conditions
Lecture 220 Various methods of data manipulation
Section 50: Python - Merging and Concatenating Dataframes
Lecture 221 Joining dataframes horizontally
Lecture 222 Concatenate dataframes vertically
Lecture 223 Merging and joining dataframes
Section 51: Python - Applied Exploratory Data Analysis Methods
Lecture 224 Frequency and percentage analysis
Lecture 225 Descriptive statistics and analysis
Lecture 226 Group by data analysis method
Lecture 227 Pivot table analysis - all in one
Lecture 228 Cross-tabulation analysis method
Lecture 229 Correlation analysis for numeric data
Lecture 230 Applied exploratory data analysis
Section 52: Python - Exploring Data Visualisations Methods
Lecture 231 Understanding visualisation tools
Lecture 232 Getting started with bar charts
Lecture 233 Stacked and clustered bar charts
Lecture 234 Pie chart for percentage analysis
Lecture 235 Line chart for grouping data analysis
Lecture 236 Exploring distribution with histogram
Lecture 237 Correlation analysis via scatterplot
Lecture 238 Matrix visualisation with heatmap
Lecture 239 Boxplot statistical visualisation method
Lecture 240 Exploring data visualisations methods
Section 53: Python - Practical Data Transformation Methods
Lecture 241 Investigating distribution of numeric data
Lecture 242 Shapiro Wilk test of normality
Lecture 243 Starting with square root transformation
Lecture 244 Logarithmic transformation method
Lecture 245 Box-cox power transformation method
Lecture 246 Yeo-Johnson power transformation method
Lecture 247 Practical data transformation methods
Section 54: Python - Statistical Tests and Hypothesis Testing
Lecture 248 One sample t-test
Lecture 249 Independent sample t-test
Lecture 250 One way Analysis of Variance
Lecture 251 Chi square test for independence
Lecture 252 Pearson correlation analysis
Lecture 253 Linear regression analysis
Lecture 254 Statistical tests and hypothesis testing
Section 55: Python - Exploring Feature Engineering Methods
Lecture 255 Generating new features
Lecture 256 Extracting day, month and year
Lecture 257 Encoding features - LabelEncoder
Lecture 258 Categorizing numeric feature
Lecture 259 Manual feature encoding
Lecture 260 Converting features into dummy
Lecture 261 Feature engineering methods
Section 56: Python - Data Preprocessing for Machine Learning
Lecture 262 Selecting features and target
Lecture 263 Scaling features - StandardScaler
Lecture 264 Scaling features - MinMaxScaler
Lecture 265 Dimensionality reduction with PCA
Lecture 266 Splitting into train and test set
Lecture 267 Preprocessing for machine learning
Section 57: Python - Supervised Regression ML Models
Lecture 268 Linear regression ML model
Lecture 269 Decision tree regressor ML model
Lecture 270 Random forest regressor ML model
Lecture 271 Supervised regression ML models
Section 58: Python - Supervised Classification ML Models
Lecture 272 Logistic regression ML model
Lecture 273 Decision tree classification ML model
Lecture 274 Random forest classification ML model
Lecture 275 Supervised classification ML models
Section 59: Python - Segmentation with KMeans Clustering
Lecture 276 Calculating within cluster sum of squares
Lecture 277 Selecting optimal number of clusters
Lecture 278 Application of KMeans machine learning
Lecture 279 Data segmentation with KMeans clustering
Section 60: Final Project - Sports Data Analytics
Section 61: What's Next?
Lecture 280 Your next steps - Portfolios
Lecture 281 Your next steps - LinkedIn
Section 62: Extra - Python Error Message
Lecture 282 ModuleNotFound error
Lecture 283 Syntax error
Lecture 284 Key error
Lecture 285 Index error
Lecture 286 Attribute error
Lecture 287 Value error
Lecture 288 Type error
Lecture 289 Resource
Section 63: Extra - Fasten Your Coding
Lecture 290 Diagnosing errors
Lecture 291 Debugging errors
Lecture 292 Enhancing codes
Lecture 293 ChatGPT prompt
Those who are interested in entering the field of data analytics and want to learn the complete tools and techniques used in the industry.,Those who are highly interested in learning complete data analytics using Excel, SQL and Python.,This course is NOT for those who are interested to learn data science or advanced machine learning application.
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