Data Science is very vast field.

I saw one linkedin profile today with below skills 👇

Technical Skills:
Data Manipulation: Numpy, Pandas, BeautifulSoup, PySpark
Data Visualization: EDA- Matplotlib, Seaborn, Plotly, Tableau, PowerBI
Machine Learning: Scikit-Learn, TimeSeries Analysis
MLOPs: Gensinms, Github Actions, Gitlab CI/CD, mlflows, WandB, comet
Deep Learning: PyTorch, TensorFlow, Keras
Natural Language Processing: NLTK, NER, Spacy, word2vec, Kmeans, KNN, DBscan
Computer Vision: openCV, Yolo-V5, unet, cnn, resnet
Version Control: Git, Github, Gitlab
Database: SQL, NOSQL, Databricks
Web Frameworks: Streamlit, Flask, FastAPI, Streamlit
Generative AI - HuggingFace, LLM, Langchain, GPT-3.5, and GPT-4
Project Management and collaboration tool- JIRA, Confluence
Deployment- AWS, GCP, Docker, Google Vertex AI, Data Robot AI, Big ML, Microsoft Azure

How many of them do you have?

How to learn data science -> build projects
How to learn machine learning-> build projects
How to learn web development -> build projects
How to learn data analytics -> build projects

Projects give you idea of how things actually work in real life. Also, give you added advantage of showcasing your learning to recruiters in future.

Agree?

Google, Harvard, and even OpenAI are offering FREE Generative AI courses
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https://t.iss.one/generativeai_gpt/26

Do you guys believe in 80-20 rule (Pareto rule)?

Eg- For Data Scientist/ Analyst, 80% of time involve data cleaning and 20% actually doing analytics & delivering insights.

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Are you a free member and still haven’t had the GPT4-o rolled out to you yet?

Click this link and it should force it to roll out to you and become available!

Share this with anyone who’s still waiting to try it out.

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Have you ever used scaling in any data science project?

Here are some widely used scaling techniques.

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Data Scientist Problems and Tools 🧵

🧹 Data Cleaning - Pandas
📊 Data Visualization - Matplotlib
📈 Statistical Analysis - SciPy
🤖 Machine Learning - Scikit-Learn
🧠 Deep Learning - TensorFlow
💾 Big Data Processing - Apache Spark
📝 Natural Language Processing - NLTK
🚀 Model Deployment - Flask
🔀 Version Control - GitHub
🗄️ Data Storage - PostgreSQL
☁️ Cloud Computing - AWS
🧪 Experiment Tracking - MLflow

How to be Top 1% in 2024 📈

• Workout
• Meditation
• Daily Sun
• No alcohol
• Productivity
• 8hours Sleep
• Chase goals
• Spend time with family
• Discipline
• Selflove

Agree?? 🤔💭

Essential Data Science Key Concepts

1. Data: Data is the raw information that is collected and stored. It can be structured (in databases or spreadsheets) or unstructured (text, images, videos). Data can be quantitative (numbers) or qualitative (descriptions).

2. Data Cleaning: Data cleaning involves identifying and correcting errors in the dataset, handling missing values, removing outliers, and ensuring data quality before analysis.

3. Data Exploration: Data exploration involves summarizing the main characteristics of the data, understanding data distributions, identifying patterns, and detecting correlations or relationships within the data.

4. Descriptive Statistics: Descriptive statistics are used to describe and summarize the main features of a dataset. This includes measures like mean, median, mode, standard deviation, and visualization techniques.

5. Data Visualization: Data visualization is the graphical representation of data to help in understanding patterns, trends, and insights. Common visualization tools include bar charts, histograms, scatter plots, and heatmaps.

6. Statistical Inference: Statistical inference involves drawing conclusions from data with uncertainty. It includes hypothesis testing, confidence intervals, and regression analysis to make predictions or draw insights from data.

7. Machine Learning: Machine learning is a subset of artificial intelligence that uses algorithms to learn from data and make predictions or decisions without being explicitly programmed. It includes supervised learning, unsupervised learning, and reinforcement learning.

8. Feature Engineering: Feature engineering is the process of selecting, transforming, and creating features (input variables) to improve model performance in machine learning tasks.

9. Model Evaluation: Model evaluation involves assessing the performance of a machine learning model using metrics like accuracy, precision, recall, F1 score, ROC-AUC, and confusion matrix.

10. Data Preprocessing: Data preprocessing involves preparing the data for analysis or modeling. This includes encoding categorical variables, scaling numerical data, and splitting the data into training and testing sets.

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Identifying outliers in a data science project is an important step to ensure the quality and accuracy of your analysis. Outliers can be caused by measurement errors, data entry mistakes, or even intentional manipulation. Here are some approaches you can use to identify liars in your data science project:

1. Visual Exploration: Start by visualizing your data using plots such as histograms, box plots, or scatter plots. Look for any data points that appear significantly different from the majority of the data. Outliers may appear as points that are far away from the main cluster or exhibit unusual patterns.

2. Statistical Methods: Utilize statistical methods to identify outliers. One common approach is to calculate the z-score or standard deviation of each data point and flag those that fall outside a certain threshold (e.g., more than 3 standard deviations away). Another method is the interquartile range (IQR), where data points outside the range of 1.5 times the IQR are considered outliers.

3. Domain Knowledge: Leverage your domain expertise to identify potential outliers. If you have a good understanding of the data and the context in which it was collected, you may be able to identify values that are implausible or inconsistent with what is expected.

4. Machine Learning Techniques: You can use machine learning algorithms to detect outliers. Unsupervised learning algorithms like clustering or density-based methods (e.g., DBSCAN) can help identify unusual patterns or clusters in the data that may indicate outliers.

5. Data Validation: Cross-check your data with external sources or known benchmarks. If possible, compare your data with other reliable sources or conduct external validation to verify its accuracy and consistency.

6. Outlier Detection Models: Train outlier detection models on your dataset. These models can learn patterns from the majority of the data and flag any observations that deviate significantly from those patterns.

It's important to note that not all outliers are necessarily liars or errors; some may represent valid and interesting data points. It's crucial to carefully investigate and understand the reasons behind the outliers before making any decisions about their treatment or exclusion from the analysis.

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ENJOY LEARNING 👍👍

In machine learning projects, the fit, transform, predict, and fit_transform methods are commonly used in the context of preprocessing data, training models, and making predictions. Here's a brief overview of how to use each of these methods in your machine learning projects:

1. fit: The fit method is used to train a model or estimator on the training data. It learns the parameters of the model based on the input data and is typically followed by the transform or predict method. For example, when using a machine learning algorithm like linear regression or support vector machine, you would call the fit method to train the model on your training data.

2. transform: The transform method is used to apply transformations to the data based on what was learned during the fit step. This method is typically used for feature engineering, data preprocessing, or scaling. For example, if you have trained a scaler object on your training data using the fit method, you can then use the transform method to scale your test data based on the parameters learned from the training data.

3. predict: The predict method is used to make predictions on new, unseen data using a trained model. After training your model using the fit method, you can use the predict method to generate predictions for new input data. For example, if you have trained a classification model on a dataset, you can use the predict method to classify new instances.

4. fit_transform: The fit_transform method combines the fit and transform steps into a single operation. It first fits the transformation based on the training data and then applies the transformation to the same data. This method is commonly used in preprocessing pipelines where you want to apply multiple transformations to your data in a single step. For example, when using techniques like StandardScaler or OneHotEncoder, you can use the fit_transform method to both fit and transform your data in one go.

Here's an example of how you might use these methods in a machine learning project:

from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression

# Fit and transform training data
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)

# Fit a linear regression model
model = LinearRegression()
model.fit(X_train_scaled, y_train)

# Transform test data
X_test_scaled = scaler.transform(X_test)

# Make predictions on test data
predictions = model.predict(X_test_scaled)

By understanding how to use these methods effectively in your machine learning projects, you can streamline your workflow, improve model performance, and ensure consistency in your data preprocessing and modeling steps.

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Top 7 Data Science Projects in different fields

1. Healthcare:

• Predictive Healthcare Analytics: Develop models to predict disease risk, optimize treatment plans, and identify high-risk patients.

2. Finance:

• Fraud Detection: Use machine learning algorithms to detect fraudulent transactions in financial data.

3. Retail:

• Personalized Recommendations: Create recommendation systems that predict customer preferences and suggest relevant products.

4. Transportation:

• Traffic Optimization: Develop models to predict traffic patterns and optimize transportation networks.

5. Manufacturing:

• Predictive Maintenance: Use data analysis to predict equipment failures and schedule maintenance accordingly.

6. Climate Science:

• Climate Change Modeling: Build models to simulate climate change and predict its impact on various ecosystems.

7. Social Science:

• Sentiment Analysis: Analyze social media data to understand public sentiment and identify trends.

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Top 5 pandas functions

1. read_csv(): This function is used to read data from a CSV file into a pandas DataFrame.

2. head(): This function is used to display the first few rows of a DataFrame.

3. groupby(): This function is used to group data in a DataFrame based on one or more columns and perform aggregate functions on the groups.

4. merge(): This function is used to merge two DataFrames based on a common column or index.

5. plot(): This function is used to create various types of plots such as line plots, bar plots, and scatter plots from the data in a DataFrame.

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Which one have you used the most?

Any person learning deep learning or artificial intelligence in particular, know that there are ultimately two paths that they can go:

1. Computer vision
2. Natural language processing.

I outlined a roadmap for computer vision I believe many beginners will find helpful.

Artificial Intelligence

How to get freelancing clients for data science projects
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https://t.iss.one/freelancing_upwork/17