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👍5
Top 10 essential data science terminologies
1. Machine Learning: A subset of artificial intelligence that involves building algorithms that can learn from and make predictions or decisions based on data.
2. Big Data: Extremely large datasets that require specialized tools and techniques to analyze and extract insights from.
3. Data Mining: The process of discovering patterns, trends, and insights in large datasets using various methods such as machine learning and statistical analysis.
4. Predictive Analytics: The use of statistical algorithms and machine learning techniques to predict future outcomes based on historical data.
5. Natural Language Processing (NLP): The field of study that focuses on enabling computers to understand, interpret, and generate human language.
6. Neural Networks: A type of machine learning model inspired by the structure and function of the human brain, consisting of interconnected nodes that can learn from data.
7. Feature Engineering: The process of selecting, transforming, and creating new features from raw data to improve the performance of machine learning models.
8. Data Visualization: The graphical representation of data to help users understand and interpret complex datasets more easily.
9. Deep Learning: A subset of machine learning that uses neural networks with multiple layers to learn complex patterns in data.
10. Ensemble Learning: A technique that combines multiple machine learning models to improve predictive performance and reduce overfitting.
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ENJOY LEARNING 👍👍
1. Machine Learning: A subset of artificial intelligence that involves building algorithms that can learn from and make predictions or decisions based on data.
2. Big Data: Extremely large datasets that require specialized tools and techniques to analyze and extract insights from.
3. Data Mining: The process of discovering patterns, trends, and insights in large datasets using various methods such as machine learning and statistical analysis.
4. Predictive Analytics: The use of statistical algorithms and machine learning techniques to predict future outcomes based on historical data.
5. Natural Language Processing (NLP): The field of study that focuses on enabling computers to understand, interpret, and generate human language.
6. Neural Networks: A type of machine learning model inspired by the structure and function of the human brain, consisting of interconnected nodes that can learn from data.
7. Feature Engineering: The process of selecting, transforming, and creating new features from raw data to improve the performance of machine learning models.
8. Data Visualization: The graphical representation of data to help users understand and interpret complex datasets more easily.
9. Deep Learning: A subset of machine learning that uses neural networks with multiple layers to learn complex patterns in data.
10. Ensemble Learning: A technique that combines multiple machine learning models to improve predictive performance and reduce overfitting.
Credits: https://news.1rj.ru/str/datasciencefree
ENJOY LEARNING 👍👍
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👍6
✅ Best Telegram channels to get free coding & data science resources
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✅ Free Courses with Certificate:
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👍4❤1
Why is it require to split our data into three parts: train, validation, and test?
• The training set is used to fit the model, i.e. to train the model with the data.
• The validation set is then used to provide an unbiased evaluation of a model while fine-tuning hyperparameters. This improves the generalization of the model.
• Finally, a test data set which the model has never "seen" before should be used for the final evaluation of the model. This allows for an unbiased evaluation of the model. The evaluation should never be performed on the same data that is used for training. Otherwise the model performance would not be representative.
• The training set is used to fit the model, i.e. to train the model with the data.
• The validation set is then used to provide an unbiased evaluation of a model while fine-tuning hyperparameters. This improves the generalization of the model.
• Finally, a test data set which the model has never "seen" before should be used for the final evaluation of the model. This allows for an unbiased evaluation of the model. The evaluation should never be performed on the same data that is used for training. Otherwise the model performance would not be representative.
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What are the main assumptions of linear regression?
There are several assumptions of linear regression. If any of them is violated, model predictions and interpretation may be worthless or misleading.
1) Linear relationship between features and target variable.
2) Additivity means that the effect of changes in one of the features on the target variable does not depend on values of other features. For example, a model for predicting revenue of a company have of two features - the number of items a sold and the number of items b sold. When company sells more items a the revenue increases and this is independent of the number of items b sold. But, if customers who buy a stop buying b, the additivity assumption is violated.
3) Features are not correlated (no collinearity) since it can be difficult to separate out the individual effects of collinear features on the target variable.
4) Errors are independently and identically normally distributed (yi = B0 + B1*x1i + ... + errori):
i) No correlation between errors (consecutive errors in the case of time series data).
ii) Constant variance of errors - homoscedasticity. For example, in case of time series, seasonal patterns can increase errors in seasons with higher activity.
iii) Errors are normaly distributed, otherwise some features will have more influence on the target variable than to others. If the error distribution is significantly non-normal, confidence intervals may be too wide or too narrow.
There are several assumptions of linear regression. If any of them is violated, model predictions and interpretation may be worthless or misleading.
1) Linear relationship between features and target variable.
2) Additivity means that the effect of changes in one of the features on the target variable does not depend on values of other features. For example, a model for predicting revenue of a company have of two features - the number of items a sold and the number of items b sold. When company sells more items a the revenue increases and this is independent of the number of items b sold. But, if customers who buy a stop buying b, the additivity assumption is violated.
3) Features are not correlated (no collinearity) since it can be difficult to separate out the individual effects of collinear features on the target variable.
4) Errors are independently and identically normally distributed (yi = B0 + B1*x1i + ... + errori):
i) No correlation between errors (consecutive errors in the case of time series data).
ii) Constant variance of errors - homoscedasticity. For example, in case of time series, seasonal patterns can increase errors in seasons with higher activity.
iii) Errors are normaly distributed, otherwise some features will have more influence on the target variable than to others. If the error distribution is significantly non-normal, confidence intervals may be too wide or too narrow.
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🔐"Key Python Libraries for Data Science:
Numpy: Core for numerical operations and array handling.
SciPy: Complements Numpy with scientific computing features like optimization.
Pandas: Crucial for data manipulation, offering powerful DataFrames.
Matplotlib: Versatile plotting library for creating various visualizations.
Keras: High-level neural networks API for quick deep learning prototyping.
TensorFlow: Popular open-source ML framework for building and training models.
Scikit-learn: Efficient tools for data mining and statistical modeling.
Seaborn: Enhances data visualization with appealing statistical graphics.
Statsmodels: Focuses on estimating and testing statistical models.
NLTK: Library for working with human language data.
These libraries empower data scientists across tasks, from preprocessing to advanced machine learning."
Numpy: Core for numerical operations and array handling.
SciPy: Complements Numpy with scientific computing features like optimization.
Pandas: Crucial for data manipulation, offering powerful DataFrames.
Matplotlib: Versatile plotting library for creating various visualizations.
Keras: High-level neural networks API for quick deep learning prototyping.
TensorFlow: Popular open-source ML framework for building and training models.
Scikit-learn: Efficient tools for data mining and statistical modeling.
Seaborn: Enhances data visualization with appealing statistical graphics.
Statsmodels: Focuses on estimating and testing statistical models.
NLTK: Library for working with human language data.
These libraries empower data scientists across tasks, from preprocessing to advanced machine learning."
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Top 5 data science concepts 👇
1. Machine Learning: Machine learning is a subset of artificial intelligence that focuses on developing algorithms and models that can learn from and make predictions or decisions based on data. It involves techniques such as supervised learning, unsupervised learning, and reinforcement learning to analyze and interpret patterns in data.
2. Data Visualization: Data visualization is the graphical representation of data to help users understand complex datasets and identify trends, patterns, and insights. It involves creating visualizations such as charts, graphs, maps, and dashboards to communicate data effectively and facilitate data-driven decision-making.
3. Statistical Analysis: Statistical analysis is the process of collecting, exploring, analyzing, and interpreting data to uncover patterns, relationships, and trends. It involves using statistical methods such as hypothesis testing, regression analysis, and probability theory to draw meaningful conclusions from data and make informed decisions.
4. Data Preprocessing: Data preprocessing is the initial step in the data analysis process that involves cleaning, transforming, and preparing raw data for analysis. It includes tasks such as data cleaning, feature selection, normalization, and handling missing values to ensure the quality and reliability of the data before applying machine learning algorithms.
5. Big Data: Big data refers to large and complex datasets that exceed the processing capabilities of traditional data management tools. It involves storing, processing, and analyzing massive volumes of structured and unstructured data to extract valuable insights and drive informed decision-making. Techniques such as distributed computing, parallel processing, and cloud computing are used to handle big data efficiently.
Data Science Resources for Beginners
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https://drive.google.com/drive/folders/1uCShXgmol-fGMqeF2hf9xA5XPKVSxeTo
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1. Machine Learning: Machine learning is a subset of artificial intelligence that focuses on developing algorithms and models that can learn from and make predictions or decisions based on data. It involves techniques such as supervised learning, unsupervised learning, and reinforcement learning to analyze and interpret patterns in data.
2. Data Visualization: Data visualization is the graphical representation of data to help users understand complex datasets and identify trends, patterns, and insights. It involves creating visualizations such as charts, graphs, maps, and dashboards to communicate data effectively and facilitate data-driven decision-making.
3. Statistical Analysis: Statistical analysis is the process of collecting, exploring, analyzing, and interpreting data to uncover patterns, relationships, and trends. It involves using statistical methods such as hypothesis testing, regression analysis, and probability theory to draw meaningful conclusions from data and make informed decisions.
4. Data Preprocessing: Data preprocessing is the initial step in the data analysis process that involves cleaning, transforming, and preparing raw data for analysis. It includes tasks such as data cleaning, feature selection, normalization, and handling missing values to ensure the quality and reliability of the data before applying machine learning algorithms.
5. Big Data: Big data refers to large and complex datasets that exceed the processing capabilities of traditional data management tools. It involves storing, processing, and analyzing massive volumes of structured and unstructured data to extract valuable insights and drive informed decision-making. Techniques such as distributed computing, parallel processing, and cloud computing are used to handle big data efficiently.
Data Science Resources for Beginners
👇👇
https://drive.google.com/drive/folders/1uCShXgmol-fGMqeF2hf9xA5XPKVSxeTo
Share with credits: https://news.1rj.ru/str/datasciencefun
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The first channel on Telegram that offers exciting questions, answers, and tests in data science, artificial intelligence, machine learning, and programming languages
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New developers: whenever you work on something interesting, write it down in a document which you keep updating. This will be very helpful when you need to create a resume or have to talk about your achievements in an interview. (Or for college essays.)
I can guarantee you that if you don't do this, you will forget half the interesting things you've done; and for a majority of us, our brains are experts in convincing us that we haven't really done anything interesting.
I can guarantee you that if you don't do this, you will forget half the interesting things you've done; and for a majority of us, our brains are experts in convincing us that we haven't really done anything interesting.
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🔥WEBSITES TO GET FREE DATA SCIENCE CERTIFICATIONS🔥
👌. Kaggle: http://kaggle.com
👌. freeCodeCamp: http://freecodecamp.org
👌. Cognitive Class: http://cognitiveclass.ai
👌. Microsoft Learn: http://learn.microsoft.com
👌. Google's Learning Platform: https://developers.google.com/learn
👌. Kaggle: http://kaggle.com
👌. freeCodeCamp: http://freecodecamp.org
👌. Cognitive Class: http://cognitiveclass.ai
👌. Microsoft Learn: http://learn.microsoft.com
👌. Google's Learning Platform: https://developers.google.com/learn
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I often get asked- what's the BEST Certification for #datascience or #machinelearning?
👉My answer is: none
The reality is that certification don't matter for data science.
This is not commerce. we are not using the same techniques over and over again to solve well-defined problems.
The problems are challenging, the data is messy and numerous techniques are used.
So if you've wondering which certification you should get, Save yourself,some mental energy and stop thinking about it- they are not really matter.
👉 Instead, grab a dataset and start playing with it.
👉 Start applying what you know and trying to solve interesting problems, learn something new every day.
👉 Here are few places to grab datasets to get you started
Google: https://toolbox.google.com/datasetsearch
Kaggle: https://www.kaggle.com/datasets
US Government Dataset: www.data.gov
Quandl: https://www.quandl.com/
UCI
ML repo: http://mlr.cs.umass.edu/ml/datasets.html
World Bank🏦: https://data.worldbank.org/
👉My answer is: none
The reality is that certification don't matter for data science.
This is not commerce. we are not using the same techniques over and over again to solve well-defined problems.
The problems are challenging, the data is messy and numerous techniques are used.
So if you've wondering which certification you should get, Save yourself,some mental energy and stop thinking about it- they are not really matter.
👉 Instead, grab a dataset and start playing with it.
👉 Start applying what you know and trying to solve interesting problems, learn something new every day.
👉 Here are few places to grab datasets to get you started
Google: https://toolbox.google.com/datasetsearch
Kaggle: https://www.kaggle.com/datasets
US Government Dataset: www.data.gov
Quandl: https://www.quandl.com/
UCI
ML repo: http://mlr.cs.umass.edu/ml/datasets.html
World Bank🏦: https://data.worldbank.org/
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Ai revolution and learning path 📚
The current AI revolution is exhilarating 🚀, pushing the boundaries of what's possible across different sectors. Yet, it's essential to anchor oneself in the foundational elements that enable these advancements:
- Neural Networks: Grasp the basics and variations, understanding how they process information and learning about key types like CNNs and RNNs 🧠.
- Loss Functions and Optimization: Familiarize yourself with how loss functions measure model performance and the role of optimization techniques like gradient descent in improving accuracy 🔍.
- Activation Functions: Learn about the significance of activation functions such as ReLU and Sigmoid in capturing non-linear patterns 🔑.
- Training and Evaluation: Master the nuanced art of model training, from preventing overfitting with regularization to fine-tuning hyperparameters for optimal performance 🎯.
- Data Handling: Recognize the importance of data preprocessing and augmentation in enhancing model robustness. 💾
- Stay Updated: Keep an eye on emerging trends, like transformers and GANs, and understand the ethical considerations in AI application. 🌐
Immersing yourself in these core areas not only prepares you for the ongoing AI wave but sets a solid foundation for navigating future advancements. Balancing a strong grasp of fundamental concepts with an awareness of new technologies is key to thriving in the AI domain.
The current AI revolution is exhilarating 🚀, pushing the boundaries of what's possible across different sectors. Yet, it's essential to anchor oneself in the foundational elements that enable these advancements:
- Neural Networks: Grasp the basics and variations, understanding how they process information and learning about key types like CNNs and RNNs 🧠.
- Loss Functions and Optimization: Familiarize yourself with how loss functions measure model performance and the role of optimization techniques like gradient descent in improving accuracy 🔍.
- Activation Functions: Learn about the significance of activation functions such as ReLU and Sigmoid in capturing non-linear patterns 🔑.
- Training and Evaluation: Master the nuanced art of model training, from preventing overfitting with regularization to fine-tuning hyperparameters for optimal performance 🎯.
- Data Handling: Recognize the importance of data preprocessing and augmentation in enhancing model robustness. 💾
- Stay Updated: Keep an eye on emerging trends, like transformers and GANs, and understand the ethical considerations in AI application. 🌐
Immersing yourself in these core areas not only prepares you for the ongoing AI wave but sets a solid foundation for navigating future advancements. Balancing a strong grasp of fundamental concepts with an awareness of new technologies is key to thriving in the AI domain.
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Here's a step-by-step beginner's roadmap for learning machine learning:🪜📚
Learn Python: Start by learning Python, as it's the most popular language for machine learning. There are many resources available online, including tutorials, courses, and books.
Understand Basic Math: Familiarize yourself with basic mathematics concepts like algebra, calculus, and probability. This will form the foundation for understanding machine learning algorithms.
Learn NumPy, Pandas, and Matplotlib: These are essential libraries for data manipulation, analysis, and visualization in Python. Get comfortable with them as they are widely used in machine learning projects.
Study Linear Algebra and Statistics: Dive deeper into linear algebra and statistics, as they are fundamental to understanding many machine learning algorithms.
Introduction to Machine Learning: Start with courses or tutorials that introduce you to machine learning concepts such as supervised learning, unsupervised learning, and reinforcement learning.
Explore Scikit-learn: Scikit-learn is a powerful Python library for machine learning. Learn how to use its various algorithms for tasks like classification, regression, and clustering.
Hands-on Projects: Start working on small machine learning projects to apply what you've learned. Kaggle competitions and datasets are great resources for this.
Deep Learning Basics: Dive into deep learning concepts and frameworks like TensorFlow or PyTorch. Understand neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
Advanced Topics: Explore advanced machine learning topics such as ensemble methods, dimensionality reduction, and generative adversarial networks (GANs).
Stay Updated: Machine learning is a rapidly evolving field, so it's important to stay updated with the latest research papers, blogs, and conferences.
🧠👀Remember, the key to mastering machine learning is consistent practice and experimentation. Start with simple projects and gradually tackle more complex ones as you gain confidence and expertise. Good luck on your learning journey!
Learn Python: Start by learning Python, as it's the most popular language for machine learning. There are many resources available online, including tutorials, courses, and books.
Understand Basic Math: Familiarize yourself with basic mathematics concepts like algebra, calculus, and probability. This will form the foundation for understanding machine learning algorithms.
Learn NumPy, Pandas, and Matplotlib: These are essential libraries for data manipulation, analysis, and visualization in Python. Get comfortable with them as they are widely used in machine learning projects.
Study Linear Algebra and Statistics: Dive deeper into linear algebra and statistics, as they are fundamental to understanding many machine learning algorithms.
Introduction to Machine Learning: Start with courses or tutorials that introduce you to machine learning concepts such as supervised learning, unsupervised learning, and reinforcement learning.
Explore Scikit-learn: Scikit-learn is a powerful Python library for machine learning. Learn how to use its various algorithms for tasks like classification, regression, and clustering.
Hands-on Projects: Start working on small machine learning projects to apply what you've learned. Kaggle competitions and datasets are great resources for this.
Deep Learning Basics: Dive into deep learning concepts and frameworks like TensorFlow or PyTorch. Understand neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
Advanced Topics: Explore advanced machine learning topics such as ensemble methods, dimensionality reduction, and generative adversarial networks (GANs).
Stay Updated: Machine learning is a rapidly evolving field, so it's important to stay updated with the latest research papers, blogs, and conferences.
🧠👀Remember, the key to mastering machine learning is consistent practice and experimentation. Start with simple projects and gradually tackle more complex ones as you gain confidence and expertise. Good luck on your learning journey!
👍19❤1
Interview questions with answers for Statistics 👇
1. Describe the central limit theorem and its importance in statistics. How does it relate to data analysis?
2. Explain the difference between denoscriptive and inferential statistics. Provide examples of each.
3. What is the purpose of hypothesis testing? Can you walk me through the steps involved in hypothesis testing?
4. What is p-value in hypothesis testing? How do you interpret p-values?
5. What is the difference between Type I and Type II errors? Can you provide examples of each?
6. How would you determine if a dataset is normally distributed? What graphical and statistical methods can you use?
7. Explain the difference between correlation and causation. How would you determine if there is a causal relationship between two variables?
8. What is the difference between population and sample? Why is it important to understand this difference in data analysis?
9. What are the measures of central tendency? When would you use each one (mean, median, mode)?
10. Describe a situation where you would use regression analysis. What are some common regression techniques, and how do you interpret their results?
11. Can you explain the concept of standard deviation? How is it related to variance, and what does it indicate about the data?
12. What is the purpose of ANOVA (Analysis of Variance)? How does it differ from regression analysis?
13. How would you deal with missing data in a dataset? What are some common imputation techniques?
14. Explain the difference between a parametric and non-parametric test. When would you choose one over the other?
15. What is the purpose of data normalization and standardization? Can you explain some common methods for achieving this?
Below you can find the answers 😊
1. Central Limit Theorem (CLT): States that regardless of the distribution of the population, the distribution of sample means approaches a normal distribution as sample size increases. It's crucial for making reliable inferences from sample data.
2. Denoscriptive vs. Inferential Statistics: Denoscriptive statistics summarize data, like mean or median, while inferential statistics make predictions or inferences about a population based on sample data.
3. Hypothesis Testing: A method to test a claim about a population parameter using sample data. It involves formulating null and alternative hypotheses, collecting data, and drawing conclusions based on statistical analysis.
4. P-value: Probability of obtaining the observed results (or more extreme) if the null hypothesis is true. It helps determine the significance of results in hypothesis testing.
5. Type I and Type II Errors: Type I error is rejecting a true null hypothesis, while Type II error is failing to reject a false null hypothesis.
6. Normality Testing: Graphical methods like histograms or statistical tests like Shapiro-Wilk can be used to check if data is normally distributed.
7. Correlation vs. Causation: Correlation measures the relationship between variables, while causation indicates one variable causing changes in another. Establishing causation requires controlled experiments.
8. Population vs. Sample: Population includes all individuals of interest, while a sample is a subset of the population. Understanding this difference is crucial for making generalizations about the population.
9. Measures of Central Tendency: Mean, median, and mode represent the center of a dataset. Mean is suitable for normally distributed data, median for skewed data, and mode for categorical data.
10. Regression Analysis: Used to model the relationship between variables. Common techniques include linear regression, logistic regression, and polynomial regression.
11. Standard Deviation: Measures the spread of data around the mean. It's the square root of the variance and indicates the variability of data points.
12. ANOVA: Analyzes differences in means among multiple groups. It differs from regression by comparing means across groups instead of modeling relationships between variables.
1. Describe the central limit theorem and its importance in statistics. How does it relate to data analysis?
2. Explain the difference between denoscriptive and inferential statistics. Provide examples of each.
3. What is the purpose of hypothesis testing? Can you walk me through the steps involved in hypothesis testing?
4. What is p-value in hypothesis testing? How do you interpret p-values?
5. What is the difference between Type I and Type II errors? Can you provide examples of each?
6. How would you determine if a dataset is normally distributed? What graphical and statistical methods can you use?
7. Explain the difference between correlation and causation. How would you determine if there is a causal relationship between two variables?
8. What is the difference between population and sample? Why is it important to understand this difference in data analysis?
9. What are the measures of central tendency? When would you use each one (mean, median, mode)?
10. Describe a situation where you would use regression analysis. What are some common regression techniques, and how do you interpret their results?
11. Can you explain the concept of standard deviation? How is it related to variance, and what does it indicate about the data?
12. What is the purpose of ANOVA (Analysis of Variance)? How does it differ from regression analysis?
13. How would you deal with missing data in a dataset? What are some common imputation techniques?
14. Explain the difference between a parametric and non-parametric test. When would you choose one over the other?
15. What is the purpose of data normalization and standardization? Can you explain some common methods for achieving this?
Below you can find the answers 😊
1. Central Limit Theorem (CLT): States that regardless of the distribution of the population, the distribution of sample means approaches a normal distribution as sample size increases. It's crucial for making reliable inferences from sample data.
2. Denoscriptive vs. Inferential Statistics: Denoscriptive statistics summarize data, like mean or median, while inferential statistics make predictions or inferences about a population based on sample data.
3. Hypothesis Testing: A method to test a claim about a population parameter using sample data. It involves formulating null and alternative hypotheses, collecting data, and drawing conclusions based on statistical analysis.
4. P-value: Probability of obtaining the observed results (or more extreme) if the null hypothesis is true. It helps determine the significance of results in hypothesis testing.
5. Type I and Type II Errors: Type I error is rejecting a true null hypothesis, while Type II error is failing to reject a false null hypothesis.
6. Normality Testing: Graphical methods like histograms or statistical tests like Shapiro-Wilk can be used to check if data is normally distributed.
7. Correlation vs. Causation: Correlation measures the relationship between variables, while causation indicates one variable causing changes in another. Establishing causation requires controlled experiments.
8. Population vs. Sample: Population includes all individuals of interest, while a sample is a subset of the population. Understanding this difference is crucial for making generalizations about the population.
9. Measures of Central Tendency: Mean, median, and mode represent the center of a dataset. Mean is suitable for normally distributed data, median for skewed data, and mode for categorical data.
10. Regression Analysis: Used to model the relationship between variables. Common techniques include linear regression, logistic regression, and polynomial regression.
11. Standard Deviation: Measures the spread of data around the mean. It's the square root of the variance and indicates the variability of data points.
12. ANOVA: Analyzes differences in means among multiple groups. It differs from regression by comparing means across groups instead of modeling relationships between variables.
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13. Dealing with Missing Data: Techniques like mean, median, or mode imputation, or more advanced methods like multiple imputation or k-nearest neighbors imputation can be used.
14. Parametric vs. Non-parametric Tests: Parametric tests assume specific data distributions, while non-parametric tests do not. Parametric tests are more powerful but require data to meet certain assumptions.
15. Data Normalization and Standardization: Techniques to scale data to a common range or standardize it with mean 0 and standard deviation 1. Common methods include min-max scaling and z-score standardization.
Like for more 😄
14. Parametric vs. Non-parametric Tests: Parametric tests assume specific data distributions, while non-parametric tests do not. Parametric tests are more powerful but require data to meet certain assumptions.
15. Data Normalization and Standardization: Techniques to scale data to a common range or standardize it with mean 0 and standard deviation 1. Common methods include min-max scaling and z-score standardization.
Like for more 😄
👍22❤1
Data Scientist Roadmap
|
|-- 1. Basic Foundations
| |-- a. Mathematics
| | |-- i. Linear Algebra
| | |-- ii. Calculus
| | |-- iii. Probability
| | `-- iv. Statistics
| |
| |-- b. Programming
| | |-- i. Python
| | | |-- 1. Syntax and Basic Concepts
| | | |-- 2. Data Structures
| | | |-- 3. Control Structures
| | | |-- 4. Functions
| | | `-- 5. Object-Oriented Programming
| | |
| | `-- ii. R (optional, based on preference)
| |
| |-- c. Data Manipulation
| | |-- i. Numpy (Python)
| | |-- ii. Pandas (Python)
| | `-- iii. Dplyr (R)
| |
| `-- d. Data Visualization
| |-- i. Matplotlib (Python)
| |-- ii. Seaborn (Python)
| `-- iii. ggplot2 (R)
|
|-- 2. Data Exploration and Preprocessing
| |-- a. Exploratory Data Analysis (EDA)
| |-- b. Feature Engineering
| |-- c. Data Cleaning
| |-- d. Handling Missing Data
| `-- e. Data Scaling and Normalization
|
|-- 3. Machine Learning
| |-- a. Supervised Learning
| | |-- i. Regression
| | | |-- 1. Linear Regression
| | | `-- 2. Polynomial Regression
| | |
| | `-- ii. Classification
| | |-- 1. Logistic Regression
| | |-- 2. k-Nearest Neighbors
| | |-- 3. Support Vector Machines
| | |-- 4. Decision Trees
| | `-- 5. Random Forest
| |
| |-- b. Unsupervised Learning
| | |-- i. Clustering
| | | |-- 1. K-means
| | | |-- 2. DBSCAN
| | | `-- 3. Hierarchical Clustering
| | |
| | `-- ii. Dimensionality Reduction
| | |-- 1. Principal Component Analysis (PCA)
| | |-- 2. t-Distributed Stochastic Neighbor Embedding (t-SNE)
| | `-- 3. Linear Discriminant Analysis (LDA)
| |
| |-- c. Reinforcement Learning
| |-- d. Model Evaluation and Validation
| | |-- i. Cross-validation
| | |-- ii. Hyperparameter Tuning
| | `-- iii. Model Selection
| |
| `-- e. ML Libraries and Frameworks
| |-- i. Scikit-learn (Python)
| |-- ii. TensorFlow (Python)
| |-- iii. Keras (Python)
| `-- iv. PyTorch (Python)
|
|-- 4. Deep Learning
| |-- a. Neural Networks
| | |-- i. Perceptron
| | `-- ii. Multi-Layer Perceptron
| |
| |-- b. Convolutional Neural Networks (CNNs)
| | |-- i. Image Classification
| | |-- ii. Object Detection
| | `-- iii. Image Segmentation
| |
| |-- c. Recurrent Neural Networks (RNNs)
| | |-- i. Sequence-to-Sequence Models
| | |-- ii. Text Classification
| | `-- iii. Sentiment Analysis
| |
| |-- d. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
| | |-- i. Time Series Forecasting
| | `-- ii. Language Modeling
| |
| `-- e. Generative Adversarial Networks (GANs)
| |-- i. Image Synthesis
| |-- ii. Style Transfer
| `-- iii. Data Augmentation
|
|-- 5. Big Data Technologies
| |-- a. Hadoop
| | |-- i. HDFS
| | `-- ii. MapReduce
| |
| |-- b. Spark
| | |-- i. RDDs
| | |-- ii. DataFrames
| | `-- iii. MLlib
| |
| `-- c. NoSQL Databases
| |-- i. MongoDB
| |-- ii. Cassandra
| |-- iii. HBase
| `-- iv. Couchbase
|
|-- 6. Data Visualization and Reporting
| |-- a. Dashboarding Tools
| | |-- i. Tableau
| | |-- ii. Power BI
| | |-- iii. Dash (Python)
| | `-- iv. Shiny (R)
| |
| |-- b. Storytelling with Data
| `-- c. Effective Communication
|
|-- 7. Domain Knowledge and Soft Skills
| |-- a. Industry-specific Knowledge
| |-- b. Problem-solving
| |-- c. Communication Skills
| |-- d. Time Management
| `-- e. Teamwork
|
`-- 8. Staying Updated and Continuous Learning
|-- a. Online Courses
|-- b. Books and Research Papers
|-- c. Blogs and Podcasts
|-- d. Conferences and Workshops
`-- e. Networking and Community Engagement
|
|-- 1. Basic Foundations
| |-- a. Mathematics
| | |-- i. Linear Algebra
| | |-- ii. Calculus
| | |-- iii. Probability
| | `-- iv. Statistics
| |
| |-- b. Programming
| | |-- i. Python
| | | |-- 1. Syntax and Basic Concepts
| | | |-- 2. Data Structures
| | | |-- 3. Control Structures
| | | |-- 4. Functions
| | | `-- 5. Object-Oriented Programming
| | |
| | `-- ii. R (optional, based on preference)
| |
| |-- c. Data Manipulation
| | |-- i. Numpy (Python)
| | |-- ii. Pandas (Python)
| | `-- iii. Dplyr (R)
| |
| `-- d. Data Visualization
| |-- i. Matplotlib (Python)
| |-- ii. Seaborn (Python)
| `-- iii. ggplot2 (R)
|
|-- 2. Data Exploration and Preprocessing
| |-- a. Exploratory Data Analysis (EDA)
| |-- b. Feature Engineering
| |-- c. Data Cleaning
| |-- d. Handling Missing Data
| `-- e. Data Scaling and Normalization
|
|-- 3. Machine Learning
| |-- a. Supervised Learning
| | |-- i. Regression
| | | |-- 1. Linear Regression
| | | `-- 2. Polynomial Regression
| | |
| | `-- ii. Classification
| | |-- 1. Logistic Regression
| | |-- 2. k-Nearest Neighbors
| | |-- 3. Support Vector Machines
| | |-- 4. Decision Trees
| | `-- 5. Random Forest
| |
| |-- b. Unsupervised Learning
| | |-- i. Clustering
| | | |-- 1. K-means
| | | |-- 2. DBSCAN
| | | `-- 3. Hierarchical Clustering
| | |
| | `-- ii. Dimensionality Reduction
| | |-- 1. Principal Component Analysis (PCA)
| | |-- 2. t-Distributed Stochastic Neighbor Embedding (t-SNE)
| | `-- 3. Linear Discriminant Analysis (LDA)
| |
| |-- c. Reinforcement Learning
| |-- d. Model Evaluation and Validation
| | |-- i. Cross-validation
| | |-- ii. Hyperparameter Tuning
| | `-- iii. Model Selection
| |
| `-- e. ML Libraries and Frameworks
| |-- i. Scikit-learn (Python)
| |-- ii. TensorFlow (Python)
| |-- iii. Keras (Python)
| `-- iv. PyTorch (Python)
|
|-- 4. Deep Learning
| |-- a. Neural Networks
| | |-- i. Perceptron
| | `-- ii. Multi-Layer Perceptron
| |
| |-- b. Convolutional Neural Networks (CNNs)
| | |-- i. Image Classification
| | |-- ii. Object Detection
| | `-- iii. Image Segmentation
| |
| |-- c. Recurrent Neural Networks (RNNs)
| | |-- i. Sequence-to-Sequence Models
| | |-- ii. Text Classification
| | `-- iii. Sentiment Analysis
| |
| |-- d. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
| | |-- i. Time Series Forecasting
| | `-- ii. Language Modeling
| |
| `-- e. Generative Adversarial Networks (GANs)
| |-- i. Image Synthesis
| |-- ii. Style Transfer
| `-- iii. Data Augmentation
|
|-- 5. Big Data Technologies
| |-- a. Hadoop
| | |-- i. HDFS
| | `-- ii. MapReduce
| |
| |-- b. Spark
| | |-- i. RDDs
| | |-- ii. DataFrames
| | `-- iii. MLlib
| |
| `-- c. NoSQL Databases
| |-- i. MongoDB
| |-- ii. Cassandra
| |-- iii. HBase
| `-- iv. Couchbase
|
|-- 6. Data Visualization and Reporting
| |-- a. Dashboarding Tools
| | |-- i. Tableau
| | |-- ii. Power BI
| | |-- iii. Dash (Python)
| | `-- iv. Shiny (R)
| |
| |-- b. Storytelling with Data
| `-- c. Effective Communication
|
|-- 7. Domain Knowledge and Soft Skills
| |-- a. Industry-specific Knowledge
| |-- b. Problem-solving
| |-- c. Communication Skills
| |-- d. Time Management
| `-- e. Teamwork
|
`-- 8. Staying Updated and Continuous Learning
|-- a. Online Courses
|-- b. Books and Research Papers
|-- c. Blogs and Podcasts
|-- d. Conferences and Workshops
`-- e. Networking and Community Engagement
👍93❤2
Have you ever thought about this?... 🤔
When you think about the data scientist role, you probably think about AI and fancy machine learning models. And when you think about the data analyst role, you probably think about good-looking dashboards with plenty of features and insights.
Well, this all looks good until you land a job, and you quickly realize that you will spend probably 60-70% of your time doing something that is called DATA CLEANING... which I agree, it’s not the sexiest topic to talk about.
The thing is that logically, if we spend so much time preparing our data before creating a dashboard or a machine learning model, this means that data cleaning becomes arguably the number one skill for data specialists. And this is exactly why today we will start a series about the most important data cleaning techniques that you will use in the workplace.
So, here is why we need to clean our data 👇🏻
1️⃣ Precision in Analysis: Clean data minimizes errors and ensures accurate results, safeguarding the integrity of the analytical process.
2️⃣ Maintaining Professional Credibility: The validity of your findings impacts your reputation in data science; unclean data can jeopardize your credibility.
3️⃣ Optimizing Computational Efficiency: Well-formatted data streamlines analysis, akin to a decluttered workspace, making processes run faster, especially with advanced algorithms.
When you think about the data scientist role, you probably think about AI and fancy machine learning models. And when you think about the data analyst role, you probably think about good-looking dashboards with plenty of features and insights.
Well, this all looks good until you land a job, and you quickly realize that you will spend probably 60-70% of your time doing something that is called DATA CLEANING... which I agree, it’s not the sexiest topic to talk about.
The thing is that logically, if we spend so much time preparing our data before creating a dashboard or a machine learning model, this means that data cleaning becomes arguably the number one skill for data specialists. And this is exactly why today we will start a series about the most important data cleaning techniques that you will use in the workplace.
So, here is why we need to clean our data 👇🏻
1️⃣ Precision in Analysis: Clean data minimizes errors and ensures accurate results, safeguarding the integrity of the analytical process.
2️⃣ Maintaining Professional Credibility: The validity of your findings impacts your reputation in data science; unclean data can jeopardize your credibility.
3️⃣ Optimizing Computational Efficiency: Well-formatted data streamlines analysis, akin to a decluttered workspace, making processes run faster, especially with advanced algorithms.
👍26❤1