Introduction to the Course
NumPy and Pandas are two of the most widely used libraries in Python for data manipulation and analysis. NumPy is used for numerical operations, while Pandas is used for data manipulation and analysis in a more structured form, such as DataFrames.
Who is this course for?
- Freshers with any Graduation degree
- Professionals who want to switch from NON-IT to IT
- Professionals who want to boost there career
Requirements
- Basic understanding of Python Programming Language
- We'll use Anaconda & Jupyter Notebooks (a free, user-friendly coding environment)
- Familiarity with basic Python knowledge is strongly recommended
- Basic Programming concepts like Variables, Data Types and Basic Arithmetic
What You'll Learn
Python Quick Refresh▶
- Introduction to python.
- Setting up Python.
- Managing Directories in Jupyter Notebook.
- Working with different datatypes.
- Arithmetic Operators.
- Comparison Operators.
- Logical Operators.
- Conditional statements.
- Lists.
- Dictionaries.
- Tuples.
- Build-in Functions.
- User-defined Functions.
Essentials Python Libraries▶
- Installing Libraries.
- Importing Libraries.
- Pandas Library.
- NumPy Library.
- Pandas vs NumPy.
- Matplotlib Library.
- Seaborn Library.
Fundamental NumPy Properties▶
- Introduction to NumPy arrays.
- Creating NumPy arrays.
- Indexing NumPy arrays.
- Array shape.
- Iterating Over NumPy Arrays.
Mathematics for Data Science▶
- Basic NumPy arrays: zeros().
- Basic NumPy arrays: ones().
- Basic NumPy arrays: full().
- Adding a scalar.
- Subtracting a scalar.
- Multiplying by a scalar.
- Dividing by a scalar.
- Raise to a power.
- Transpose.
- Element wise addition.
- Element wise subtraction.
- Element wise multiplication.
- Element wise division.
- Matrix multiplication.
- Statistics.
Python Pandas DataFrames & Series▶
- What is a python pandas DataFrames.
- What is a Python Pandas Series.
- DataFrame vs Series.
- Creating a DataFrame using lists.
- Creating a DataFrame using a dictionary.
- Loading CSV data into python.
- Changing the Index Column.
- Inplace.
- Examining the DataFrame: Head & Tail.
- Statistical summary of the DataFrame.
- Slicing rows using bracket operators.
- Indexing columns using bracket operators.
- Boolean list.
- Filtering Rows.
- Filtering rows using & and | operators.
- Filtering data using loc().
- Filtering data using iloc().
- Adding and deleting rows and columns.
- Sorting Values.
- Exporting and saving pandas DataFrames.
- Concatenating DataFrames.
- groupby().
Data Cleaning▶
- Introduction to Data Cleaning.
- Quality of Data.
- Examples of Anomalies.
- Median-Based Anomaly Detection.
- Mean-based anomaly detection.
- Z-score-based Anomaly Detection.
- Interquartile Range for Anomaly Detection.
- Dealing with missing values.
- Regular Expressions.
- Feature Scaling.
Data Visualization using python▶
- Introduction.
- Setting Up Matplotlib.
- Plotting Line Plots using Matplotlib.
- Title, Labels & Legend.
- Plotting Histograms.
- Plotting Bar Charts.
- Plotting Pie Charts.
- Plotting Scatter Plots.
- Plotting Log Plots.
- Ploting Polar Plots.
- Handling Dates.
- Creating multiple subplots in one figure.
Python Quick Refresh▶
- Introduction.
- What is Exploratory Data Analysis?.
- Univariate Analysis.
- Univariate Analysis: Continuous Data.
- Univariate Analysis: Categorical Data.
- Bivariate analysis: Continuous & Continuous.
- Bivariate analysis: Categorical & Categorical.
- Bivariate analysis: Continuous & Categorical.
- Detecting Outliers.
- Categorical Variable Transformation.
Time Series in Python▶
- Introduction to Time Series.
- Getting stock data using yfinance.
- Converting a Dataset into Time Series.
- Working with Time Series.
- Time Series Data Visualization with Python.
Trainer Expertise
This program is monitored by a team of professionals. We have crafted this program using the learnings of 23+ years of experience handling corporate training and job oriented training. Our students are working in almost all top MNCs across India.
Job Opportunities
100% placement record — each student successfully transitioned into a desired career role.
Course Duration
16 Weeks
Fees
Training + Job Assistance: ₹35,000
- Admission: ₹10,000
- After 1 month: ₹25,000