May Allah SWT bless you Dr Sheraz, Sir Haris, Dr Irfan Malik and whole Team. May Allah gives you highest rank here and hereafter, PROVIDES NEIGHBOURHOOD OF OUR BELOVED LAST PROPHET SAW. Aamiin
EDA (Exploratory Data Analysis): This involves getting a high-level understanding of the data to identify patterns, trends, and potential issues. Data Cleaning: This involves fixing inconsistencies, formatting the data, and handling missing entries. Preprocessing: This may involve transforming the data into a format that is suitable for analysis.
Understanding Nature of Data and its Types Understanding the nature of your data is critical before applying any techniques. Here's what you need to consider: Data Types: Numerical: Data represented by numbers, further classified into: Continuous: Can take any value within a specific range (e.g., height, weight). Discrete: Can only take specific values within a range (e.g., number of customers). Categorical: Data representing categories or classifications (e.g., eye color, product type). Textual: Data in the form of text strings (e.g., customer reviews, social media posts). Understanding Data Types helps you choose: Appropriate data visualization techniques. Statistical methods for analysis.
Here are the sub-tasks of the Analyze stage: Understand Nature of Data: This involves understanding the data types (numerical, categorical, etc.) and the relationships between the variables. Data Reduction: This may involve selecting a subset of relevant features from the data. Outliers: This may involve identifying and handling outliers, which are data points that fall far outside the expected range.
Preliminary Analysis and its Branches Preliminary analysis, often referred to as Exploratory Data Analysis (EDA), is a crucial sub-step within the "Analyze" stage of the data science process flowchart. It's where you get your hands dirty and start to understand the data you're working with. Here's a breakdown of this stage: Objectives: Gain a high-level understanding of the data's structure and content. Identify potential issues like missing values, outliers, and inconsistencies. Discover initial insights, patterns, and trends. Branches of Preliminary Analysis: Univariate Analysis: This analyzes each variable in isolation using techniques like: Summary statistics: Measures like mean, median, standard deviation to understand central tendency and spread. Visualizations: Histograms, box plots, and scatter plots to visualize data distribution and relationships. Bivariate Analysis: This explores relationships between two variables using techniques like: Scatter plots: Visualize the correlation or association between two variables. Correlation coefficients: Measure the strength and direction of the linear relationship between two variables.
Preprocessing and its Types Preprocessing prepares your data for further analysis by addressing issues and transforming it into a usable format. Here are some common preprocessing techniques: Handling Missing Values: Techniques like deletion, imputation (filling with estimates), or carrying forward/backward values. Encoding Categorical Data: Converting categorical data into numerical format for analysis (e.g., one-hot encoding, label encoding). Scaling and Normalization: Adjusting the scale of different features to a similar range to avoid bias during analysis. Feature Selection and Engineering: Selecting relevant features and creating new features from existing ones to improve model performance.
How to extract knowledge from data. The first stage is Acquire. In this stage, a data scientist recognizes the data required to solve a problem and retrieves it from various sources. This may involve collecting new data, or retrieving data from databases or online sources. The second stage is Prepare. Here, the data scientist cleans and prepares the data for analysis. This may involve fixing inconsistencies, formatting the data, and handling missing entries. The third stage is Analyze. In this stage, the data scientist explores and analyzes the data to uncover patterns and trends. This may involve using statistical methods and data visualization techniques. The fourth stage is Report. Here, the data scientist communicates the findings of the analysis. This may involve creating reports, charts, or visualizations to present the insights to stakeholders.
Tough LG rha ab..ab damag ghom rha.... Pichly lectures sy ab mushkil LG rha.. Sir ifran apki story Kam krti h.. Wrna. Sb rokha rokha LG rha data science
May Allah SWT bless you Dr Sheraz, Sir Haris, Dr Irfan Malik and whole Team.
May Allah gives you highest rank here and hereafter, PROVIDES NEIGHBOURHOOD OF OUR BELOVED LAST PROPHET SAW. Aamiin
EDA (Exploratory Data Analysis): This involves getting a high-level understanding of the data to identify patterns, trends, and potential issues.
Data Cleaning: This involves fixing inconsistencies, formatting the data, and handling missing entries.
Preprocessing: This may involve transforming the data into a format that is suitable for analysis.
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Understanding Nature of Data and its Types
Understanding the nature of your data is critical before applying any techniques. Here's what you need to consider:
Data Types:
Numerical: Data represented by numbers, further classified into:
Continuous: Can take any value within a specific range (e.g., height, weight).
Discrete: Can only take specific values within a range (e.g., number of customers).
Categorical: Data representing categories or classifications (e.g., eye color, product type).
Textual: Data in the form of text strings (e.g., customer reviews, social media posts).
Understanding Data Types helps you choose:
Appropriate data visualization techniques.
Statistical methods for analysis.
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Here are the sub-tasks of the Analyze stage:
Understand Nature of Data: This involves understanding the data types (numerical, categorical, etc.) and the relationships between the variables.
Data Reduction: This may involve selecting a subset of relevant features from the data.
Outliers: This may involve identifying and handling outliers, which are data points that fall far outside the expected range.
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Preliminary Analysis and its Branches
Preliminary analysis, often referred to as Exploratory Data Analysis (EDA), is a crucial sub-step within the "Analyze" stage of the data science process flowchart. It's where you get your hands dirty and start to understand the data you're working with. Here's a breakdown of this stage:
Objectives:
Gain a high-level understanding of the data's structure and content.
Identify potential issues like missing values, outliers, and inconsistencies.
Discover initial insights, patterns, and trends.
Branches of Preliminary Analysis:
Univariate Analysis: This analyzes each variable in isolation using techniques like:
Summary statistics: Measures like mean, median, standard deviation to understand central tendency and spread.
Visualizations: Histograms, box plots, and scatter plots to visualize data distribution and relationships.
Bivariate Analysis: This explores relationships between two variables using techniques like:
Scatter plots: Visualize the correlation or association between two variables.
Correlation coefficients: Measure the strength and direction of the linear relationship between two variables.
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Preprocessing and its Types
Preprocessing prepares your data for further analysis by addressing issues and transforming it into a usable format. Here are some common preprocessing techniques:
Handling Missing Values: Techniques like deletion, imputation (filling with estimates), or carrying forward/backward values.
Encoding Categorical Data: Converting categorical data into numerical format for analysis (e.g., one-hot encoding, label encoding).
Scaling and Normalization: Adjusting the scale of different features to a similar range to avoid bias during analysis.
Feature Selection and Engineering: Selecting relevant features and creating new features from existing ones to improve model performance.
best explanation
How to extract knowledge from data.
The first stage is Acquire. In this stage, a data scientist recognizes the data required to solve a problem and retrieves it from various sources. This may involve collecting new data, or retrieving data from databases or online sources.
The second stage is Prepare. Here, the data scientist cleans and prepares the data for analysis. This may involve fixing inconsistencies, formatting the data, and handling missing entries.
The third stage is Analyze. In this stage, the data scientist explores and analyzes the data to uncover patterns and trends. This may involve using statistical methods and data visualization techniques.
The fourth stage is Report. Here, the data scientist communicates the findings of the analysis. This may involve creating reports, charts, or visualizations to present the insights to stakeholders.
Any recommended book regarding data science?
@@lahoritouch For Beginners = Data Science by Zeeshan Usmani.
For Advance Level = Getting Started With Data Science by Murtaza Haider.
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How can I access the quizzes? Please assist me!
What are the sources of getting data
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Tough LG rha ab..ab damag ghom rha.... Pichly lectures sy ab mushkil LG rha.. Sir ifran apki story Kam krti h.. Wrna. Sb rokha rokha LG rha data science
Same
Insan zinda h Ya Mar gia... Binary Attribute