Learn Data Science Tutorial With Python
Data Science has become one of the fastest-growing fields in recent years, helping organizations to make informed decisions, solve problems and understand human behavior. As the volume of data grows so does the demand for skilled data scientists. The most common languages used for data science are Python and R.
In this Data Science with Python tutorial will guide you through the fundamentals of both data science and Python programming.
Data Science with Python
Before starting the tutorial you can refer to these articles:
Python Libraries for Data Science
To gain expertise in data science, you need to have a strong foundation in the following libraries:
- Pandas for Data Manipulation
- NumPy for Numerical Computing
- Matplotlib for Data Visualization
- Seaborn for Data Visualization
- Scikit-learn for Machine Learning
Data Loading
Data loading means importing raw data from various sources and storing it in one place for further analysis.
- Loading a CSV File into a DataFrame
- Loading Data from an Excel File
- Loading Data from JSON File
- Loading Data from SQL Databases
- Web Scraping using BeautifulSoup to Scrape Data
- Loading Data from MongoDB into DataFrame
Data Preprocessing
Data preprocessing involves cleaning and transforming raw data into a usable format for accurate and reliable analysis.
- What is Data Preprocessing?
- Working with Missing Data using Pandas
- Removing Duplicates using drop_duplicates()
- Scaling and Normalization of Data
- Aggregating and Grouping Data
- Feature Selection using Sklearn
- Handling Categorical Data using Label Encoding
- Handling Categorical Data using One-Hot Encoding
- Detecting outlier using Z score
- Detecting outlier using Interquartile Range
- Handling Imbalanced Data
- Efficient Preprocessing for Large Datasets
Data Analysis
Data analysis is the process of inspecting data to discover meaningful insights and trends to make informed decision.
- What is Data Processing?
- Exploratory Data Analysis in Python
- Univariate and Multivariate Analysis
- Calculating Correlation
- Hypothesis testing using Python
- One-sample t-test using Python
- Two Sample t-test using Python
- ANOVA (Analysis of Variance) in Python
- Mann-Whitney U Test in Python
- Z-test in Python
- Chi-Square Test
- PCA with Python
- Shapiro-Wilk Test in Python
- Wilcoxon Signed-Rank Test in Python
Data Visualization
Data visualization uses graphical representations such as charts and graphs to understand and interpret complex data.
Data Visualization using Matplotlib
Data Visualization using Seaborn
Interactive Visualization
- Scatter Plot
- Bar Chart
- Line Chart
- Animated Data Visualization
- Choropleth Maps using
- Interactive Visualization using Bokeh
- Visualizing Geospatial Data using Folium
Machine Learning
Machine learning focuses on developing algorithms that helps computers to learn from data and make predictions or decisions without explicit programming.
Related Courses: Machine Learning is an essential skill for any aspiring data analyst and data scientist and also for those who wish to transform a massive amount of raw data into trends and predictions. Learn this skill today with Machine Learning Foundation – Self Paced Course designed and curated by industry experts having years of expertise in ML and industry-based projects.
Every organisation now relies on data before making any important decisions regarding their future. So, it is safe to say that Data is really the king now. So why do you want to get left behind? This LIVE course will introduce the learner to advanced concepts like: Linear Regression, Naive Bayes & KNN, Numpy, Pandas, Matlab & much more. You will also get to work on real-life projects through the course. So wait no more, Become a Data Science Expert now.
