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.
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 Python00:01:00
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Course Materials
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Setting up Python00:02:00
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What is Jupyter?00:01:00
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Anaconda Installation: Windows, Mac & Ubuntu00:04:00
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How to implement Python in Jupyter?00:01:00
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Managing Directories in Jupyter Notebook00:03:00
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Input/Output00:02:00
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Working with different datatypes00:01:00
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Variables00:02:00
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Arithmetic Operators00:02:00
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Comparison Operators00:01:00
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Logical Operators00:03:00
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Conditional statements00:02:00
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Loops00:04:00
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Sequences: Lists00:03:00
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Sequences: Dictionaries00:03:00
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Sequences: Tuples00:01:00
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Functions: Built-in Functions00:01:00
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Functions: User-defined Functions00:04:00
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Installing Libraries00:01:00
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Importing Libraries00:02:00
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Pandas Library for Data Science00:01:00
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NumPy Library for Data Science00:01:00
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Pandas vs NumPy00:01:00
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Matplotlib Library for Data Science00:01:00
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Seaborn Library for Data Science00:01:00
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Introduction to NumPy arrays00:01:00
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Creating NumPy arrays00:06:00
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Indexing NumPy arrays00:06:00
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Array shape00:01:00
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Iterating Over NumPy Arrays00:05:00
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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 scalar00:02:00
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Adding a scalar00:02:00
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Multiplying by a scalar00:01:00
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Dividing by a scalar00:01:00
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Raise to a power00:01:00
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Transpose00:01:00
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Element wise addition00:02:00
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Element wise subtraction00:01:00
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Element wise multiplication00:01:00
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Element wise division00:01:00
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Matrix multiplication00:02:00
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Statistics00:03:00
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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 Series00:01:00
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Creating a DataFrame using lists00:03:00
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Creating a DataFrame using a dictionary00:01:00
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Loading CSV data into python00:02:00
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Changing the Index Column00:01:00
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Inplace00:01:00
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Examining the DataFrame: Head & Tail00:01:00
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Statistical summary of the DataFrame00:01:00
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Slicing rows using bracket operators00:01:00
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Indexing columns using bracket operators00:01:00
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Boolean list00:01:00
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Filtering Rows00:01:00
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Filtering rows using & and | operators00:02:00
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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 columns00:03:00
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Sorting Values00:02:00
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Exporting and saving pandas DataFrames00:02:00
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Concatenating DataFrames00:01:00
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groupby()00:03:00
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Introduction to Data Cleaning00:01:00
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Quality of Data00:01:00
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Examples of Anomalies00:01:00
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Median-based Anomaly Detection00:03:00
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Mean-based anomaly detection00:03:00
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Z-score-based Anomaly Detection00:03:00
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Interquartile Range for Anomaly Detection00:05:00
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Dealing with missing values00:06:00
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Regular Expressions00:07:00
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Feature Scaling00:03:00
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Introduction – Data Visualization using Python00:01:00
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Setting Up Matplotlib00:01:00
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Plotting Line Plots using Matplotlib00:02:00
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Title, Labels & Legend00:07:00
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Plotting Histograms00:01:00
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Plotting Bar Charts00:02:00
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Plotting Pie Charts00:03:00
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Plotting Scatter Plots00:06:00
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Plotting Log Plots00:01:00
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Plotting Polar Plots00:02:00
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Handling Dates00:01:00
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Creating multiple subplots in one figure00:03:00
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Introduction – Exploratory Data Analysis00:01:00
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What is Exploratory Data Analysis?00:01:00
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Univariate Analysis00:02:00
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Univariate Analysis: Continuous Data00:06:00
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Univariate Analysis: Categorical Data00:02:00
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Bivariate analysis: Continuous & Continuous00:05:00
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Bivariate analysis: Categorical & Categorical00:03:00
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Bivariate analysis: Continuous & Categorical00:02:00
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Detecting Outliers00:06:00
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Categorical Variable Transformation00:04:00
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Introduction to Time Series00:02:00
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Getting Stock Data using Yfinance00:03:00
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Converting a Dataset into Time Series00:04:00
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Working with Time Series00:04:00
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Time Series Data Visualization with Python00:03:00
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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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