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Data Science with Python
Overview In the evolving landscape of data science, proficiency in Python has become indispensable. This comprehensive course, “Data Science with …
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Overview
In the evolving landscape of data science, proficiency in Python has become indispensable. This comprehensive course, “Data Science with Python,” is meticulously crafted to equip learners with essential skills and knowledge required to excel in data analysis, visualization, and machine learning using Python.
The course begins by laying a solid foundation in Python programming, making it accessible even to those with no prior coding experience. You will learn fundamental concepts such as variables, data types, control structures, functions, and object-oriented programming principles. Hands-on exercises and coding challenges are strategically integrated to ensure practical understanding and proficiency in Python programming.
Moving forward, the course dives into the realm of data manipulation and analysis using powerful libraries like NumPy and Pandas. You will master techniques for cleaning messy data, transforming datasets, and performing complex operations to extract meaningful insights. Through real-world examples and projects, you will develop the skills to handle data effectively and efficiently.
Next, the course explores data visualization techniques using Matplotlib and Seaborn. You will learn to create various types of plots, charts, and graphs to visually represent data and communicate findings effectively. Understanding how to visualize data is crucial for interpreting trends, patterns, and outliers, which are essential steps in the data analysis process.
Moreover, the course delves into statistical analysis and exploratory data analysis (EDA), providing you with the tools and techniques to derive insights and make informed decisions from data. You will learn how to apply statistical methods, conduct hypothesis testing, and perform EDA to uncover hidden patterns and relationships within datasets.
Lastly, the course introduces the fundamentals of machine learning using Scikit-Learn. You will explore supervised and unsupervised learning algorithms, understand their principles, and learn to implement them to build predictive models. Practical exercises and projects will enable you to apply machine learning techniques to real-world problems, from classification and regression to clustering and dimensionality reduction.
By the end of this course, you will have a comprehensive understanding of Python for data science, equipped with the skills to manipulate data, perform exploratory analysis, create visualizations, and build machine learning models. Whether you are looking to kickstart a career in data science or enhance your analytical skills, this course will empower you to succeed in the data-driven world.
Learning Outcomes
- Master Python programming for data manipulation and analysis.
- Apply statistical methods and exploratory data analysis techniques.
- Create data visualizations using Matplotlib and Seaborn.
- Implement machine learning algorithms using Scikit-Learn.
- Build predictive models for real-world applications.
What Will Make You Stand Out?
On Completion of this online course, you’ll acquire:
- CPD QS Accredited Course.
- Upon Course Completion, Earn a Complimentary PDF and a hard copy Certificate to Showcase Your Enhanced Skills.
- Lifetime access.
- 24x7 Tutor Support.
- Complete the course at your own pace.
Description
This course is ideal for anyone looking to enter the field of data science or enhance their current skill set with Python. It begins with Python basics, ensuring even beginners can follow along, then progresses to more advanced topics like data cleaning, manipulation, and visualization. Hands-on exercises and projects throughout the course provide practical experience in applying Python to real-world data scenarios.
You will learn how to leverage Python libraries such as NumPy and Pandas for data manipulation, Matplotlib and Seaborn for data visualization, and Scikit-Learn for machine learning tasks. The emphasis is on practical application: from exploring and preparing data to building and evaluating machine learning models. By the end, you’ll be proficient in Python programming for data science, capable of tackling various data challenges and making data-driven decisions.
Upon completion, you will receive a certificate that validates your skills and knowledge in data science with Python, enhancing your credibility in the job market or within your current organization.
Who is this course for?
This course is suitable for beginners with no prior programming experience as well as professionals looking to transition into data science. It is also beneficial for analysts or researchers who want to automate data processes and gain insights efficiently using Python.
Requirements
Access to a computer with internet connectivity and a desire to learn and succeed in your home-based business venture. No prior experience or qualifications are necessary.
Certification
Upon successful completion of the Data Science with Python course, learners can obtain both a PDF certificate and a Hard copy certificate for completely FREE. The Hard copy certificate is available for a nominal fee of £3.99, which covers the delivery charge within the United Kingdom. Additional delivery charges may apply for orders outside the United Kingdom.
Career Path
- Data Analyst
- Business Analyst
- Data Scientist
Course Curriculum
| Course Introduction and Table of Contents | |||
| Course Introduction and Table of Contents | 00:09:00 | ||
| Introduction to Python, Pandas and Numpy | |||
| Introduction to Python, Pandas and Numpy | 00:07:00 | ||
| System and Environment Setup | |||
| System and Environment Setup | 00:08:00 | ||
| Python Strings | |||
| Python Strings – Part 1 | 00:11:00 | ||
| Python Strings – Part 2 | 00:09:00 | ||
| Python Numbers and Operators | |||
| Python Numbers and Operators – Part 1 | 00:06:00 | ||
| Python Numbers and Operators – Part 2 | 00:07:00 | ||
| Python Lists | |||
| Python Lists – Part 1 | 00:05:00 | ||
| Python Lists – Part 2 | 00:06:00 | ||
| Python Lists – Part 3 | 00:05:00 | ||
| Python Lists – Part 4 | 00:07:00 | ||
| Python Lists – Part 5 | 00:07:00 | ||
| Tuples in Python | |||
| Tuples in Python | 00:06:00 | ||
| Sets in Python | |||
| Sets in Python – Part 1 | 00:05:00 | ||
| Sets in Python – Part 2 | 00:04:00 | ||
| Python Dictionary | |||
| Python Dictionary – Part 1 | 00:07:00 | ||
| Python Dictionary – Part 2 | 00:07:00 | ||
| NumPy Library - Introduction | |||
| NumPy Library Intro – Part 1 | 00:05:00 | ||
| NumPy Library Intro – Part 2 | 00:05:00 | ||
| NumPy Library Intro – Part 3 | 00:06:00 | ||
| NumPy Array Operations and Indexing | |||
| NumPy Array Operations and Indexing – Part 1 | 00:04:00 | ||
| NumPy Array Operations and Indexing – Part 2 | 00:06:00 | ||
| NumPy Multi-Dimensional Arrays | |||
| NumPy Multi-Dimensional Arrays – Part 1 | 00:07:00 | ||
| NumPy Multi-Dimensional Arrays – Part 2 | 00:06:00 | ||
| NumPy Multi-Dimensional Arrays – Part 3 | 00:05:00 | ||
| Introduction to Pandas Series | |||
| Introduction to Pandas Series | 00:08:00 | ||
| Introduction to Pandas Dataframes | |||
| Introduction to Pandas Dataframes | 00:07:00 | ||
| Pandas Dataframe conversion and drop | |||
| Pandas Dataframe conversion and drop – Part 1 | 00:06:00 | ||
| Pandas Dataframe conversion and drop – Part 2 | 00:06:00 | ||
| Pandas Dataframe conversion and drop – Part 3 | 00:07:00 | ||
| Pandas Dataframe summary and selection | |||
| Pandas Dataframe summary and selection – Part 1 | 00:06:00 | ||
| Pandas Dataframe summary and selection – Part 2 | 00:06:00 | ||
| Pandas Dataframe summary and selection – Part 3 | 00:07:00 | ||
| Pandas Missing Data Management and Sorting | |||
| Pandas Missing Data Management and Sorting – Part 1 | 00:07:00 | ||
| Pandas Missing Data Management and Sorting – Part 2 | 00:07:00 | ||
| Pandas Hierarchical-Multi Indexing | |||
| Pandas Hierarchical-Multi Indexing | 00:06:00 | ||
| Pandas CSV File Read Write | |||
| Pandas CSV File Read Write – Part 1 | 00:05:00 | ||
| Pandas CSV File Read Write – Part 2 | 00:07:00 | ||
| Pandas JSON File Read Write | |||
| Pandas JSON File Read Write Operations | 00:07:00 | ||
| Pandas Concatenation Merging and Joining | |||
| Pandas Concatenation Merging and Joining – Part 1 | 00:05:00 | ||
| Pandas Concatenation Merging and Joining – Part 2 | 00:04:00 | ||
| Pandas Concatenation Merging and Joining – Part 3 | 00:04:00 | ||
| Pandas Stacking and Pivoting | |||
| Pandas Stacking and Pivoting – Part 1 | 00:06:00 | ||
| Pandas Stacking and Pivoting – Part 2 | 00:05:00 | ||
| Pandas Duplicate Data Management | |||
| Pandas Duplicate Data Management | 00:07:00 | ||
| Pandas Mapping | |||
| Pandas Mapping | 00:04:00 | ||
| Pandas Grouping | |||
| Pandas Groupby | 00:06:00 | ||
| Pandas Aggregation | |||
| Pandas Aggregation | 00:09:00 | ||
| Pandas Binning or Bucketing | |||
| Pandas Binning or Bucketing | 00:08:00 | ||
| Pandas Re-index and Rename | |||
| Pandas Re-index and Rename – Part 1 | 00:04:00 | ||
| Pandas Re-index and Rename – Part 2 | 00:05:00 | ||
| Pandas Replace Values | |||
| Pandas Replace Values | 00:05:00 | ||
| Pandas Dataframe Metrics | |||
| Pandas Dataframe Metrics | 00:07:00 | ||
| Pandas Random Permutation | |||
| Pandas Random Permutation | 00:08:00 | ||
| Pandas Excel sheet Import | |||
| Pandas Excel sheet Import | 00:07:00 | ||
| Pandas Condition Selection and Lambda Function | |||
| Pandas Condition Selection and Lambda Function – Part 1 | 00:05:00 | ||
| Pandas Condition Selection and Lambda Function – Part 2 | 00:05:00 | ||
| Pandas Ranks Min Max | |||
| Pandas Ranks Min Max | 00:06:00 | ||
| Pandas Cross Tabulation | |||
| Pandas Cross Tabulation | 00:07:00 | ||
| Matplotlib Graphs and plots | |||
| Graphs and plots using Matplotlib – Part 1 | 00:06:00 | ||
| Graphs and plots using Matplotlib – Part 2 | 00:02:00 | ||
| Matplotlib Histograms | |||
| Matplotlib Histograms | 00:03:00 | ||
Frequently Asked Questions
In the UK, the social care system is mainly managed by the local councils. People are directly employed by the councils. They often work together with the health commissioners under joint funding arrangements. Some people work for private companies or voluntary organizations hired by local councils. They help the local councils with their personal social services.
In the UK, the social care system is mainly managed by the local councils. People are directly employed by the councils. They often work together with the health commissioners under joint funding arrangements. Some people work for private companies or voluntary organizations hired by local councils. They help the local councils with their personal social services.
In the UK, the social care system is mainly managed by the local councils. People are directly employed by the councils. They often work together with the health commissioners under joint funding arrangements. Some people work for private companies or voluntary organizations hired by local councils. They help the local councils with their personal social services.
In the UK, the social care system is mainly managed by the local councils. People are directly employed by the councils. They often work together with the health commissioners under joint funding arrangements. Some people work for private companies or voluntary organizations hired by local councils. They help the local councils with their personal social services.
In the UK, the social care system is mainly managed by the local councils. People are directly employed by the councils. They often work together with the health commissioners under joint funding arrangements. Some people work for private companies or voluntary organizations hired by local councils. They help the local councils with their personal social services.
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Number of Units:
62 -
Access:
1 Year -
Duration:
6 hours, 20 minutes -
PDF Certificate
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