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Learn MoreData Analysis Nanodegree
Rating: 4.3 out of 54.3(193 ratings)
9,764 students
Data Analysis Nanodegree
The Complete Guide To Learn Data Analysis
Created byNermin Elgrawany
Last updated 9/2024
Arabic
What you'll learn
- Introduction to Data Analysis
- Statistics and probability
- Excel for data analysis
- SQL
- Python for data analysis
- R for data analysis
- Tableau
Course content
9 sections • 145 lectures • 6h 3m total length
- Course Introduction2:10
- In this Section0:49
- What is data and types of data3:45
- What is analysis and types of analysis2:00
- Data analysis process1:48
- Data Analyst Skills0:53
- Data types
- In this Section0:49
- Statistics vs. Probability0:42
- Types of statistics1:04
- Descriptive Statistics1:03
- Measures of Center3:45
- Measures of Center
- Measures of Spread5:21
- Measures of Spread
- Shape of Distribution2:57
- Shape of Distribution
- Outliers2:45
- Visualize Descriptive Statistics3:05
- Notation4:26
- Inferential Statistics3:03
- Estimation1:10
- Confidence Interval3:56
- z-test vs t-test3:29
- Confidence Interval Example.12:54
- Confidence Interval Example.20:45
- Confidence Interval
- Hypothesis Testing3:04
- Hypothesis Tests3:13
- Hypothesis Testing Example.13:05
- Hypothesis Testing Example.23:39
- Hypotheis Testing
- Statistical Significance vs. Practical Significance1:08
- Linear Regression4:04
- Correlation Coefficient vs. R-squared2:39
- Simple vs. Multiple Linear Regression1:29
- Simple Linear Regression Example3:19
In Simple Linear Regression equation b0 = 10.45 and b1 = .516 NOT 1.37
- Multiple Linear Regression Example4:20
- Linear Regression
- Probability2:38
- Basics of Probability3:47
- Basics of Probability
- Probability of Binomial Distribution2:12
- Binomial Distribution
- Conditional Probability1:49
- Conditional Probability
- In this Section0:42
- Collecting data with Excel1:24
- Cleaning data-Blanks1:06
- Cleaning data- Remove duplicates0:49
- Cleaning data-Change datatypes1:16
- Cleaning data-Dealing with text4:30
- Cleaning data-Spaces0:46
- Cleaning data-Split&Merge1:47
- Cleaning data-Dealing with date1:40
- Cleaning data-Errors0:49
- Cleaning data-Cell reference2:04
- Cleaning data-Sort&Filter1:01
- Cleaning data-Conditional formatting2:04
- Cleaning data-Transpose0:45
- Cleaning data-Vlookup1:21
- Cleaning data-Validation1:06
- Cleaning data-Window&Freeze panel1:12
- Cleaning data-Pivot table4:16
- Solver tool2:04
- Descriptive Statistics3:01
- Inferential Statistics- Confidence Interval0:58
- Inferential Statistics- Hypothesis Testing2:27
- Linear Regression2:21
- Basic Probability2:51
- Probability Distribution2:40
- Visualization5:58
- Case study0:58
It's your turn to give it a shot!
To complete this project, you have to accomplish the following tasks:
Find the best jobs by salary and company rating.
Explore skills required in job descriptions.
Predict salary based on company revenue.
- In this Section1:07
- Installing PostgreSQL1:28
- Introduction to SQL4:10
- Introduction to PostgreSQL1:54
- Import files2:43
- Basic commands2:30
- Basic commands
- Filtering-Where Statement4:59
- Where Statement
- Aggregation2:13
- Aggregation
- Joins4:49
- Joins
- Having and Group By2:09
- Having and Group By
- Case Statement1:58
- Case Statement
- Subquery3:04
- Subquery
- Data Cleaning-Database4:27
- Data Cleaning-Duplicates5:26
- Data Cleaning-Null values3:02
- Data Cleaning-Datatypes1:37
- Data Cleaning-Text commands3:12
- Data Cleaning-Date commands2:07
- Case study1:08
It's your turn to give it a shot.
Follow the instructions and answer the questions:
1- What is the average high school GPA?
2- Use your query to join the two tables and order your final table by Application No.
3- Who has the best marks in Physics?
4- Filter the table to return the top 10 students in Biology ordered ascending.
5- When the student who had the worst marks in Chemistry was born?
6- Return the number of students who have high school GPA above average.
Good luck :)
- In this Section1:37
- Installing Python1:28
- Introduction to Jupyter Notebook2:52
- Introduction to Python6:00
- Python datatypes3:03
- Python datatypes
- Operations & Operators2:02
- Operations & Operators
- Control flow-If Statement1:55
- Control flow-If Statement
- Control flow-Loops (for/while)4:37
- Control flow-Loops (for/while)
- Control flow-Create function(def/lambda)5:12
- Control flow-Create function(def/lambda)
- Methods1:19
- Intro Wrangling Data3:44
- Collecting data-Import files2:35
- Collecting data-Create dataframe3:58
- Assessing data3:22
- Cleaning data-Null values2:21
- Cleaning data-Change datatypes1:33
- Cleaning data- Remove duplicates1:51
- Cleaning data-String methods4:05
- Cleaning data-Dealing with date2:31
- Cleaning data-Slicing & Reshape4:11
- Cleaning data-Rename0:50
- Descriptive Statistics3:35
- Inferential Statistics- Confidence Interval4:50
- Inferential Statistics- Hypothesis Testing4:00
- Linear Regression3:26
- Probability2:28
- Visualization(Univariate)4:47
- Visualization(Bivariate)1:17
- Save0:59
- Python Case-study0:50
Follow the instructions and answer the questions to complete the project.
Instructions:
You can practice this project directly on Kaggle.
https://www.kaggle.com/anikannal/solar-power-generation-data
1. Load the data from the CSV files
2. Explore each dataset - columns, counts, basic stats
3. Pre-process the data to allow for some analysis (hint: date and time)
4. Try to answer the questions.
5. Explain your insights through visualizations.
6. Create a report or a presentation of your findings.
7. Communicate your findings with colleagues.
Questions:
• What is the mean value of daily yield?
• What is the total irradiation per day?
• What is the max ambient and module temperature?
• How many inverters are there for each plant?
• What is the maximum/minimum amount of DC/AC Power generated in a time interval/day?
• Which inverter (source-key) has produced maximum DC/AC power?
• Rank the inverters based on the DC/AC power they produce
• Is there any missing data?
Good luck :)
- In this Section1:01
- Installing R1:07
- Orientation of RStudio2:34
- Introduction to R3:51
- Intro- Operations & Operators1:40
- Intro- If Statement2:04
- Intro - Loops(for, while)3:06
- Intro- Create function1:31
- Collecting data-Import files2:12
- Collecting data-Create dataframe3:15
- Assessing data2:29
- Cleaning data- Duplicates, Null values, Datatypes4:00
- Cleaning data-Subset & Reshape4:47
- Cleaning data- String functions2:17
- Descriptive Statistics2:32
- Inferential Statistics3:33
- Linear Regression3:17
- Visualization(Univariate)2:22
- Visualization(Bivariate)4:00
- R: Case-study0:35
100,000 UK Used Car Data set
Scraped data of used cars, 100,000 listings, which have been separated into files corresponding to each car manufacturer.
1- Open the link https://www.kaggle.com/adityadesai13/used-car-dataset-ford-and-mercedes
2- Download file(s) of your selection.
3- Read file(s) and explore the dataset.
4- Use functions for cleaning data (If needed)
5- Accomplish the task:
Make a tool to predict how much my friend should sell his old car for compared to other stuff on the market, and then just extended the data set, then made a more general car value regression model.
6- Support your findings with suitable visualizations.
7- Create a presentation or a report with your findings.
8- Share your work with colleagues.
- In this Section1:01
- Introduction to Data Visualization2:54
- Installing Tableau Public0:41
- Start page1:11
- Data source page2:14
- Woorksheet4:20
- Sheet1-Bar chart3:24
- Sheet2-Pie chart1:38
- Sheet3-Line chart2:18
- Dashboard1:35
- Story1:10
- Case study1:07
- Finale0:53
Requirements
- PC or laptop
- Internet connection
- Basic knowledge of Excel, PowerPoint
Description
This course aims to excel the primary skills in the field of Data Analysis through different tools and techniques such as Statistics, Excel, SQL, Python, R and Tableau. Whether you are a newbie to Data Analysis or looking for promoting your career, this course will take you in a journey step by step to the level of professional Data Analysts.
Who this course is for:
- Anyone wants to start the Data Analysis journey