Course Highlights
Mastering Python alongside its powerful libraries, NumPy and Pandas, has become a gateway to success in today’s data-driven world. With organisations worldwide heavily relying on data for strategic decisions, this course, Data Manipulation in Python: Master Python, NumPy & Pandas, equips you with the skills to extract, clean, and analyse data effectively. Whether in finance, healthcare, technology, or marketing, proficiency in Python for data manipulation ensures you stay competitive in evolving job markets.
This course will guide you through key concepts like managing datasets with Pandas, performing advanced mathematical operations with NumPy, and exploring structured and unstructured data using Python. You’ll also delve into data visualisation and time series analysis, enabling you to uncover actionable insights. These modules provide you with the ability to tackle complex challenges in data science and analytics, boosting your employability across industries experiencing growing demand for data professionals.
By the end of this course, you’ll possess the tools to transform your career. Data manipulation with Python, combined with Pandas and NumPy, is an essential skill for aspiring data analysts, data scientists, and business intelligence professionals. With the global economy leaning increasingly towards data-centric strategies, this expertise can lead to better job prospects, higher salaries, and a more fulfilling career path.
Learning outcome
- Understand Python’s role in data science and master its key functionalities.
- Gain proficiency in Pandas for efficient data handling and analysis.
- Utilize Numpy for numerical computations and array manipulation.
- Work with time-series data to analyze trends and forecast outcomes.
- Visualize data using Python to communicate findings effectively.
Course media
Why should I take this course?
- Build expertise in Python, the most in-demand programming language for data science.
- Master Numpy and Pandas to handle complex data tasks with ease.
- Open doors to high-paying roles in data analysis and business intelligence.
- You will learn the researched and proven approach adopted by successful people to transform their careers.
- Stay ahead in a data-driven job market with advanced analytics skills.
Certificate of Achievement
Skill Up Recognised Certificate
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CPD Quality Standards Accredited Certificate
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Requirements
- Basic understanding of programming concepts.
- Access to a computer with Python installed.
- Willingness to learn and apply data analysis skills.
Course Curriculum
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Welcome to the course!
00:01:00
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Introduction to Python
00:01:00
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Course Materials
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Setting up Python
00:02:00
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What is Jupyter?
00:01:00
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Anaconda Installation: Windows, Mac & Ubuntu
00:04:00
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How to implement Python in Jupyter?
00:01:00
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Managing Directories in Jupyter Notebook
00:03:00
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Input/Output
00:02:00
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Working with different datatypes
00:01:00
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Variables
00:02:00
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Arithmetic Operators
00:02:00
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Comparison Operators
00:01:00
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Logical Operators
00:03:00
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Conditional statements
00:02:00
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Loops
00:04:00
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Sequences: Lists
00:03:00
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Sequences: Dictionaries
00:03:00
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Sequences: Tuples
00:01:00
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Functions: Built-in Functions
00:01:00
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Functions: User-defined Functions
00:04:00
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Installing Libraries
00:01:00
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Importing Libraries
00:02:00
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Pandas Library for Data Science
00:01:00
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NumPy Library for Data Science
00:01:00
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Pandas vs NumPy
00:01:00
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Matplotlib Library for Data Science
00:01:00
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Seaborn Library for Data Science
00:01:00
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Introduction to NumPy arrays
00:01:00
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Creating NumPy arrays
00:06:00
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Indexing NumPy arrays
00:06:00
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Array shape
00:01:00
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Iterating Over NumPy Arrays
00:05:00
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Basic NumPy arrays: zeros()
00:02:00
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Basic NumPy arrays: ones()
00:01:00
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Basic NumPy arrays: full()
00:01:00
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Adding a scalar
00:02:00
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Adding a scalar
00:02:00
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Multiplying by a scalar
00:01:00
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Dividing by a scalar
00:01:00
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Raise to a power
00:01:00
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Transpose
00:01:00
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Element wise addition
00:02:00
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Element wise subtraction
00:01:00
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Element wise multiplication
00:01:00
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Element wise division
00:01:00
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Matrix multiplication
00:02:00
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Statistics
00:03:00
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What is a Python Pandas DataFrame?
00:01:00
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What is a Python Pandas Series?
00:01:00
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DataFrame vs Series
00:01:00
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Creating a DataFrame using lists
00:03:00
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Creating a DataFrame using a dictionary
00:01:00
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Loading CSV data into python
00:02:00
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Changing the Index Column
00:01:00
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Inplace
00:01:00
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Examining the DataFrame: Head & Tail
00:01:00
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Statistical summary of the DataFrame
00:01:00
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Slicing rows using bracket operators
00:01:00
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Indexing columns using bracket operators
00:01:00
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Boolean list
00:01:00
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Filtering Rows
00:01:00
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Filtering rows using & and | operators
00:02:00
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Filtering data using loc()
00:04:00
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Filtering data using iloc()
00:02:00
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Adding and deleting rows and columns
00:03:00
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Sorting Values
00:02:00
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Exporting and saving pandas DataFrames
00:02:00
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Concatenating DataFrames
00:01:00
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groupby()
00:03:00
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Introduction to Data Cleaning
00:01:00
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Quality of Data
00:01:00
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Examples of Anomalies
00:01:00
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Median-based Anomaly Detection
00:03:00
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Mean-based anomaly detection
00:03:00
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Z-score-based Anomaly Detection
00:03:00
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Interquartile Range for Anomaly Detection
00:05:00
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Dealing with missing values
00:06:00
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Regular Expressions
00:07:00
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Feature Scaling
00:03:00
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Introduction – Data Visualization using Python
00:01:00
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Setting Up Matplotlib
00:01:00
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Plotting Line Plots using Matplotlib
00:02:00
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Title, Labels & Legend
00:07:00
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Plotting Histograms
00:01:00
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Plotting Bar Charts
00:02:00
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Plotting Pie Charts
00:03:00
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Plotting Scatter Plots
00:06:00
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Plotting Log Plots
00:01:00
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Plotting Polar Plots
00:02:00
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Handling Dates
00:01:00
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Creating multiple subplots in one figure
00:03:00
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Introduction – Exploratory Data Analysis
00:01:00
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What is Exploratory Data Analysis?
00:01:00
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Univariate Analysis
00:02:00
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Univariate Analysis: Continuous Data
00:06:00
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Univariate Analysis: Categorical Data
00:02:00
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Bivariate analysis: Continuous & Continuous
00:05:00
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Bivariate analysis: Categorical & Categorical
00:03:00
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Bivariate analysis: Continuous & Categorical
00:02:00
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Detecting Outliers
00:06:00
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Categorical Variable Transformation
00:04:00
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Introduction to Time Series
00:02:00
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Getting Stock Data using Yfinance
00:03:00
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Converting a Dataset into Time Series
00:04:00
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Working with Time Series
00:04:00
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Time Series Data Visualization with Python
00:03:00
14-Day Money-Back Guarantee
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Duration:3 hours, 58 minutes
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Access:1 Year
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Units:107


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