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If you're a data science beginner, Python is the best programming language to get started.
Here are 7 Python libraries for data science you need to know if you want to learn:
- Data analysis
- Data visualization
- Machine learning
- Deep learning
NumPy
NumPy is a library for numerical computing in Python, providing support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.
Pandas
Widely used library for data manipulation and analysis, offering data structures like DataFrame and Series that simplify handling of structured data and performing tasks such as filtering, grouping, and merging.
Matplotlib
Powerful plotting library for creating static, interactive, and animated visualizations in Python, enabling data scientists to generate a wide variety of plots, charts, and graphs to explore and communicate data effectively.
Scikit-learn
Comprehensive machine learning library that includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and model selection, as well as utilities for data preprocessing and evaluation.
Seaborn
Built on top of Matplotlib, Seaborn provides a high-level interface for creating attractive and informative statistical graphics, making it easier to generate complex visualizations with minimal code.
TensorFlow or PyTorch
TensorFlow, Keras, or PyTorch are three prominent deep learning frameworks utilized by data scientists to construct, train, and deploy neural networks for various applications, each offering distinct advantages and capabilities tailored to different preferences and requirements.
SciPy
Collection of mathematical algorithms and functions built on top of NumPy, providing additional capabilities for optimization, integration, interpolation, signal processing, linear algebra, and more, which are commonly used in scientific computing and data analysis workflows.
Enjoy 😄👍
7 Free Kaggle Micro-Courses for Data Science Beginners with Certification
Python
https://www.kaggle.com/learn/python
Pandas
https://www.kaggle.com/learn/pandas
Data visualization
https://www.kaggle.com/learn/data-visualization
Intro to sql
https://www.kaggle.com/learn/intro-to-sql
Advanced Sql
https://www.kaggle.com/learn/advanced-sql
Intro to ML
https://www.kaggle.com/learn/intro-to-machine-learning
Advanced ML
https://www.kaggle.com/learn/intermediate-machine-learning
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Machine Learning Models Regularisation Methods 👆
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Top 10 Websites for Data Science 👇
1. Flowing Data (http://flowingdata.com)
2. Data Simplifier (http://www.datasimplifier.com)
3. R-Bloggers (http://www.r-bloggers.com)
4. Edwin Chen (http://blog.echen.me)
5. Hunch (http://hunch.net)
6. KDNuggets (http://www.kdnuggets.com)
7. Data Science Central (http://www.datasciencecentral.com)
8. Kaggle Competitions (https://www.kaggle.com/competitions)
9. Simply Statistics (http://simplystatistics.org)
10. FastML (http://fastml.com)
Most asked Python Interview Questions 👆
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Essential Data Science Skills 👆
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DATA SCIENCE JOBS ARE EXPLODING! 🤯💸
• Data Scientist: $118,399
• Data Analyst: $85,000
• Machine Learning Engineer: $123,117
• Business Intelligence Analyst: $97,000
• AI Researcher: $99,518
Top Ways Land a High-Paying Data Science Job:
1. Master Python & SQL
• Learn Pandas, NumPy, and Matplotlib.
• SQL is essential for handling databases.
2. Take Online Data Science Courses
• Platforms like Coursera, Udacity, and edX offer top courses.
• Certifications from Google or IBM add value.
3. Build a Strong Portfolio
• Work on real-world projects (Kaggle competitions, dashboards).
• Share projects on GitHub and LinkedIn.
4. Gain Experience with Internships & Freelance Work
• Apply for analyst roles or freelance on Upwork.
• Contribute to open-source projects.
5. Network & Stay Ahead
• Join data science meetups & LinkedIn groups.
• Follow industry leaders like Andrew Ng & Hadley Wickham.
Extra Tip: By Specializing in deep learning or NLP, you will stand out!
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𝗜𝗕𝗠 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍
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Learn Data Science in 2025
𝟭. 𝗔𝗽𝗽𝗹𝘆 𝗣𝗮𝗿𝗲𝘁𝗼'𝘀 𝗟𝗮𝘄 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗝𝘂𝘀𝘁 𝗘𝗻𝗼𝘂𝗴𝗵 📚
Pareto's Law states that "that 80% of consequences come from 20% of the causes".
This law should serve as a guiding framework for the volume of content you need to know to be proficient in data science.
Often rookies make the mistake of overspending their time learning algorithms that are rarely applied in production. Learning about advanced algorithms such as XLNet, Bayesian SVD++, and BiLSTMs, are cool to learn.
But, in reality, you will rarely apply such algorithms in production (unless your job demands research and application of state-of-the-art algos).
For most ML applications in production - especially in the MVP phase, simple algos like logistic regression, K-Means, random forest, and XGBoost provide the biggest bang for the buck because of their simplicity in training, interpretation and productionization.
So, invest more time learning topics that provide immediate value now, not a year later.
𝟮. 𝗙𝗶𝗻𝗱 𝗮 𝗠𝗲𝗻𝘁𝗼𝗿 ⚡
There’s a Japanese proverb that says “Better than a thousand days of diligent study is one day with a great teacher.” This proverb directly applies to learning data science quickly.
Mentors can teach you about how to build a model in production and how to manage stakeholders - stuff that you don’t often read about in courses and books.
So, find a mentor who can teach you practical knowledge in data science.
𝟯. 𝗗𝗲𝗹𝗶𝗯𝗲𝗿𝗮𝘁𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 ✍️
If you are serious about growing your excelling in data science, you have to put in the time to nurture your knowledge. This means that you need to spend less time watching mindless videos on TikTok and spend more time reading books and watching video lectures.
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𝟳 𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍
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Data Science – Essential Topics 🚀
1️⃣ Data Collection & Processing
Web scraping, APIs, and databases
Handling missing data, duplicates, and outliers
Data transformation and normalization
2️⃣ Exploratory Data Analysis (EDA)
Descriptive statistics (mean, median, variance, correlation)
Data visualization (bar charts, scatter plots, heatmaps)
Identifying patterns and trends
3️⃣ Feature Engineering & Selection
Encoding categorical variables
Scaling and normalization techniques
Handling multicollinearity and dimensionality reduction
4️⃣ Machine Learning Model Building
Supervised learning (classification, regression)
Unsupervised learning (clustering, anomaly detection)
Model selection and hyperparameter tuning
5️⃣ Model Evaluation & Performance Metrics
Accuracy, precision, recall, F1-score, ROC-AUC
Cross-validation and bias-variance tradeoff
Confusion matrix and error analysis
6️⃣ Deep Learning & Neural Networks
Basics of artificial neural networks (ANNs)
Convolutional neural networks (CNNs) for image processing
Recurrent neural networks (RNNs) for sequential data
7️⃣ Big Data & Cloud Computing
Working with large datasets (Hadoop, Spark)
Cloud platforms (AWS, Google Cloud, Azure)
Scalable data pipelines and automation
8️⃣ Model Deployment & Automation
Model deployment with Flask, FastAPI, or Streamlit
Monitoring and maintaining machine learning models
Automating data workflows with Airflow
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Data Science Projects based on domain 👆
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Use these ChatGPT Prompts To 10X your Interview Chances
1. Company research
Prompt: "I have an interview with [company] for the position of [job].
Please summarize the company's mission, its main products or services, and its recent news or achievements by analyzing its website [website link] and any recent press release."
2. Resume Optimization
Prompt: "Review my current attached resume and suggest improvements tailored to applying for a [job] at [company]. Highlight gaps in my experience and recommend ways to fill them through online courses or projects."
3. Writing the cover letter
Prompt: "Based on the job description for [job title] at [company], generate a cover letter that highlights my relevant experience, skills, and why I am passionate about working for [company]."
4. Interview preparation
Prompt: "For [job title] at [company], what are some industry-specific challenges or trends I should be aware of? How can I demonstrate my understanding or propose possible solutions during the interview?"
5. Behavioral Interview Questions
Prompt: "Create a set of behavioural interview questions relevant to the [job] role at [company]. Include a brief guide on how to structure answers using the STAR (Situation, Task, Action, Result) method, tailored to my needs." experiences."
6. Craft Your Resume Perfectly
Prompt: "I want to tailor my resume to specific job descriptions so I get shortlisted more often. Analyze this job posting for [insert job title], extract the most important keywords and skills, and help me rewrite my resume to match it perfectly while maintaining authenticity."
7. Data-Driven Job Search
Prompt: "I want to use data and hiring trends to increase my chances of landing a high-paying job in [insert industry]. Provide me with data-backed job search strategies, salary benchmarks, and negotiation tips based on market trends."
8. Network Like a Pro
Prompt: "I want to build relationships with influential professionals in [insert industry] to increase my chances of getting a job.
Give me a step-by-step networking strategy, including outreach messages, follow-ups, and ways to provide value to them."
9. Craft the Perfect Elevator Pitch
Prompt: "I need a powerful 30-second elevator pitch that instantly impresses interviewers for [insert job title]. Craft a clear, concise, and compelling pitch that highlights my skills, experience, and what makes me unique."
10. The 30-Day Job Search Plan
Prompt: "I need to land a high-paying job in [insert industry] within 30 days. Create a daily action plan that includes networking, outreach, applications, and personal branding strategies to maximize my chances of success."
#aiprompts #jobs
Key data science programming languages and tools
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𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗤𝗟 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝟱 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗪𝗲𝗯𝘀𝗶𝘁𝗲𝘀!😍
Want to boost your data skills without spending a dime?
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Essential skills for Data jobs 👆
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𝗣𝗼𝘄𝗲𝗿𝗕𝗜 𝗧𝗼𝗽 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍
Here are two FREE Power BI courses that will teach you everything you need to know!
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The New Data Scientist of 2025:
- Business background
- Self-educated
- Knows enough SQL
- Knows enough Python
- Knows enough machine learning
- Uses Microsoft Excel
- Uses AI to be productive
- Title isn't "Data Scientist"
𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 😍
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Top 10 Python Libraries for Data Science & Machine Learning
1. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
2. Pandas: Pandas is a powerful data manipulation library that provides data structures like DataFrame and Series, which make it easy to work with structured data. It offers tools for data cleaning, reshaping, merging, and slicing data.
3. Matplotlib: Matplotlib is a plotting library for creating static, interactive, and animated visualizations in Python. It allows you to generate various types of plots, including line plots, bar charts, histograms, scatter plots, and more.
4. Scikit-learn: Scikit-learn is a machine learning library that provides simple and efficient tools for data mining and data analysis. It includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and model selection.
5. TensorFlow: TensorFlow is an open-source machine learning framework developed by Google. It enables you to build and train deep learning models using high-level APIs and tools for neural networks, natural language processing, computer vision, and more.
6. Keras: Keras is a high-level neural networks API that runs on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit. It allows you to quickly prototype deep learning models with minimal code and easily experiment with different architectures.
7. Seaborn: Seaborn is a data visualization library based on Matplotlib that provides a high-level interface for creating attractive and informative statistical graphics. It simplifies the process of creating complex visualizations like heatmaps, violin plots, and pair plots.
8. Statsmodels: Statsmodels is a library that focuses on statistical modeling and hypothesis testing in Python. It offers a wide range of statistical models, including linear regression, logistic regression, time series analysis, and more.
9. XGBoost: XGBoost is an optimized gradient boosting library that provides an efficient implementation of the gradient boosting algorithm. It is widely used in machine learning competitions and has become a popular choice for building accurate predictive models.
10. NLTK (Natural Language Toolkit): NLTK is a library for natural language processing (NLP) that provides tools for text processing, tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, and more. It is a valuable resource for working with textual data in data science projects.
Data Science Resources for Beginners
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