Prerequisites & Tools for Data Science
To gain expertise in the field of data science. firstly, you need to have a strong foundation in various aspects of data science. which includes knowledge of query languages like:- SQL, programming languages like R and python, and as well as visualization tools like:- PowerBI, Quilsense, Quilview, and Tableau. Additionally, having a basic understanding of statistics for machine learning is crucial. To effectively apply machine learning algorithms, it is essential to practice and implement them with use cases relevant to your desired domain.
- What is Data Analysis
- Data Analysis using Python
- Steps of Data Analysis Process
- Importing Data
- Data processing
- Data visualization
- Why is It Important?
- Data Visualization using Matplotlib
- Style Plots using Matplotlib
- Line chart in matplotlib
- Bar Plot in Matplotlib
- Box Plot in Python using matplotlib
- Scatter Plot in Matplotlib
- Heatmap in Matplotlib
- Three-dimensional Plotting using Matplotlib
- Seaborn Kdeplot
- Data Visualization with Python Seaborn
- Interactive Data Visualization with Bokeh
- Time Series Plot or Line plot with Pandas
- Exploratory Data Analysis
- Importing Data
- Data processing using R
- Data visualization using R
- Exploratory Data Analysis in R
- Mean, Standard Deviation and Variance — Implementation
- Derivative and Function minimization
- Probability Distributions[Set 1, Set 2, Set 3]
- Confidence Intervals
- Correlation and Covariance
- Random Variables
- Hypothesis Testing
- Chi-squared Test
- ANOVA Test
- ANOVA Test using Python[One-way, Two-way]
- ANOVA Test using R
- F-Stats
- Supervised Learning
- Regression
- Linear Regression
- Regression Trees
- Non-Linear Regression
- Bayesian Linear Regression
- Polynomial Regression[Using Python, Using R]
- Classification
- Neural Networks
- Regression
- Unsupervised Learning
- Decision Tree
- Introduction to Deep Learning
- Introduction to Artificial Neutral Networks
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks
- Generative Adversarial Networks (GANs)
- Radial Basis Function Networks (RBFNs)
- Multilayer Perceptrons (MLPs)
- Deep Learning with Python OpenCV
- Pneumonia Detection using Deep Learning
- Introduction to Natural Language Processing
- Natural Language Processing
- Applications of NLP
- NLP Libraries
- Scikit-learn
- Natural language Toolkit (NLTK)
- Pattern
- TextBlob
- Query
- Text Preprocessing in Python | Set – 1
- Text Preprocessing in Python | Set 2
- Syntax Tree – Natural Language Processing
- Translation and Natural Language Processing using Google
- NLP analysis of Restaurant reviews
Some project Ideas for Beginner in Data Science – link
Data Science for Beginners
Data Science is a domain that comprises many sub-domains such as artificial intelligence, machine learning, statistics, data visualization, and analytics as well as provides practical examples and exercises to help you apply these concepts in the real world. Over the past few years, there has been tremendous demand for data scientists. To improve business efficiency it becomes important to analyze the data.
In this data science tutorial, we will provide a comprehensive overview of the core concepts, tools, and techniques used in the field of data science.
Data Science is a field that involves extracting insights and knowledge from data using various techniques and tools. If you are a beginner in Data Science, here are some steps you can follow to get started:
- Learn Programming: Programming is a fundamental skill for Data Science. Python is the most commonly used programming language in Data Science, and it has several libraries that are useful for Data Science, such as NumPy, Pandas, and Scikit-learn. You can start by learning the basics of Python programming.
- Learn Statistics: Statistics is the foundation of Data Science. Understanding statistical concepts such as mean, median, variance, and standard deviation is crucial for working with data. You can start by learning the basics of statistics.
- Learn Data Visualization: Data visualization is an essential skill for Data Science. It helps to understand patterns and trends in data. There are several libraries in Python that are useful for Data Visualization, such as Matplotlib and Seaborn.
- Learn Machine Learning: Machine learning is the core of Data Science. It involves building models that can learn from data and make predictions. There are several types of machine learning algorithms, such as supervised learning, unsupervised learning, and reinforcement learning. You can start by learning the basics of machine learning.
- Practice with Projects: Practice is essential for learning Data Science. You can start by working on small projects such as data cleaning, data analysis, and machine learning models. Kaggle is a platform where you can find data science projects and competitions to practice your skills.
- Learn from the Community: The Data Science community is very active, and there are several resources available to learn from. You can join online communities such as Reddit, LinkedIn, or Twitter. You can also attend local Data Science meetups and events.
- Continuously Learn: Data Science is a rapidly evolving field, and new techniques and tools are constantly emerging. Therefore, it’s essential to keep learning and stay updated with the latest trends and developments in Data Science.
In summary, learning Data Science involves programming, statistics, data visualization, machine learning, practice, learning from the community, and continuous learning. With dedication and consistent effort, you can become proficient in Data Science and start building solutions to real-world problems.
By the end of this tutorial, you’ll have a solid understanding of the key concepts and tools used in data science for beginners, and be well on your way to becoming proficient in the field.
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