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Data Analysis with Python and Pandas

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Overview Python is a popular programming language and Python developers are in demand these days. Data analysis is a skill …

Overview

Python is a popular programming language and Python developers are in demand these days. Data analysis is a skill that will boost your employability. Data Analysis with Python and Pandas course is designed for learners who have an interest in data analysis and familiarity with the programming language that they are ready to begin. The course will take learners through the basics of Panda before moving onto the more complex functions such as creating and navigating data frames.

It is a step by step approach and learners gain an in-depth understanding of data analysis, and using Python to manipulate your data. Learning data analysis will give learners a much in-demand skill that they can put to practical use in any business in the world.  If you’ve spent time learning Microsoft Excel and want to advance in your data analysis skills, this course is perfect for you.

Python is a widely used programming language, and will give learners an in-depth understanding of dozens of datasets and learners will realise how easy it is to get started with the course. Data Analysis with Python and Pandas will enable learners to take their Python programming and data analysis skills to the next level.

  • Why consider learning at Study 365?
  • Learning Outcomes
  • Access Duration
  • Who is this Course aimed at?
  • Entry Requirement
  • Method of Assessment
  • Certification & Awarding Body
  • Tutor Info
  • Progression and Career Path
  • Other Benefits

With so many commitments in our lives, we may not have the time to learn new skills. The beauty of studying online is that you get to find this balance between your education and your daily commitments

Study 365 offers hundreds of online courses to students across the world. Gaining new skills has never been this easy as many of our courses are open to students with little or no qualifications or previous experience. All the courses are up-to-date, relevant, affordable, and will build on existing expertise or give you a jump-start to a new career. The courses are meticulously designed and equals what is offered in a classroom setting. With a solid reputation that has established and made us made us one of the most trusted and reliable online course providers we offer the most convenient path to gain recognised skills and training that will give you the opportunity to put into practice your knowledge and expertise in your chosen career. You can learn at your own pace at Study 365 and you will be provided with all the necessary material, tutorials, qualified course instructor, narrated e-learning modules, interactive quizzes and free resources which include Free CV writing pack, free career support and course demo to make your learning experience enriching and more rewarding.

  • Learn efficient python data analysis
  • Manipulate data sets quickly and easily
  • Master python data mining
  • Gain a skill set in Python that can be used for various other applications
  • Python data analytics made simple

The course will be directly delivered to you, and you have 12 months access to the online learning platform from the date you joined the course.  The course is self-paced and you can complete it in stages, revisiting the lectures at any time.

  • This course is ideal for Data Analysts and Business Analysts
  • Python developers
  • Anyone who wants to learn Python programming
  • Basic experience in Microsoft Excel like Pivot Tables for example
  • Basic experience in Python programming

At the end of the course, you will have one assignment to be submitted (you need a mark of 65% to pass) and you can submit the assignment at any time. You will only need to pay £19 for assessment and certification when you submit the assignment. You will receive the results within 72 hours of submittal, and will be sent a certificate in 7-14 days if you have successfully passed.

Those who successfully complete the course will be awarded the Data Analysis with Python and Pandas certificate by CPD & iAP. The qualification will make you valuable to employers, and your motivation at gaining new skills will be recognised.

Harrison Kinsley is a husband, runner, friend of all dogs, programmer, teacher, and entrepreneur.

Harrison utilized his love for learning and building with technology to start multiple businesses, all of which leverage the Python programming language. Python and programming is a major part of his life and work. He believes programming is a super power, and the social impact of making this education easily accessible to anyone is one of the most important things he can do with his life.

Once you successfully complete the Data Analysis with Python and Pandas you will gain an accredited qualification that will give your career the jumpstart you have always wanted. With this qualification you can further expand your education or go onto work in numerous positions that will also put you in line to demand a higher salary or job promotion. The average UK salary per annum according to https://www.payscale.com/career-path-planner is given below.

  • .Net Programmer - £30,875 per annum
  • Web Developer - £24,833 per annum
  • Data Analyst - £25,511 per annum
  • Business Analyst - £35,543 per annum
  • Computer Programmer - £30,400 per annum
  • Software Engineer - £32,516 per annum
  • Software Developer - £24,833 per annum
  • Written and designed by the industry’s finest expert instructors with over 15 years of experience
  • Repeat and rewind all your lectures and enjoy a personalised learning experience
  • Gain access to quality video tutorials
  • Unlimited 12 months access from anywhere, anytime
  • Save time and money on travel
  • Learn at your convenience and leisure
  • Eligible for TOTUM discount card
  • Free Career Support Service
  • 25% Discount on personal Statement and covering letter writing service
  • Free Access to Over 150 courses for 2 days (48 hours)
  • Free access to course before you purchase (For selected courses only)

Course Curriculum

1: Introduction To The Course
1.1 Course Introduction 00:00:00
1.2 Getting Pandas and Fundamentals 00:00:00
1.3 Section Conclusion 00:00:00
2: Introduction To Pandas
2.1 Section introduction 00:00:00
2.2 Creating and Navigating a Dataframe 00:00:00
2.3 Slices, head and tail 00:00:00
2.4 Indexing 00:00:00
2.5 Visualizing The Data 00:00:00
2.6 Converting To Python List Or Pandas Series 00:00:00
2.7 Section Conclusion 00:00:00
3: Io Tools
3.1 Section introduction 00:00:00
3.2 Read Csv And To Csv 00:00:00
3.3 io operations 00:00:00
3.4 Read_hdf and to_hdf 00:00:00
3.5 Read Json And To Json 00:00:00
3.6 Read Pickle And To Pickle 00:00:00
3.7 Section Conclusion 00:00:00
4: Pandas Operations
4.1 Section introduction 00:00:00
4.2 Column Manipulation (Operatings on columns, creating new ones) 00:00:00
4.3 Column and Dataframe logical categorization 00:00:00
4.4 Statistical Functions Against Data 00:00:00
4.5 Moving and rolling statistics 00:00:00
4.6 Rolling apply 00:00:00
4.7 Section Outro 00:00:00
5: Handling For Missing Data / Outliers
5.1 Section Intro 00:00:00
5.2 drop na 00:00:00
5.3 Filling Forward And Backward Na 00:00:00
5.4 detecting outliers 00:00:00
5.5 Section Conclusion 00:00:00
6: Combining Dataframes
6.1 Section Introduction 00:00:00
6.2 Concatenation 00:00:00
6.3 Appending data frames 00:00:00
6.4 Merging dataframes 00:00:00
6.5 Joining dataframes 00:00:00
6.6 Section Conclusion 00:00:00
7: Advanced Operations
7.1 Section Introduction 00:00:00
7.2 Basic Sorting 00:00:00
7.3 Sorting by multiple rules 00:00:00
7.4 Resampling basics time and how (mean, sum etc) 00:00:00
7.5 Resampling to ohlc 00:00:00
7.6 Correlation and Covariance Part 1 00:00:00
7.7 Correlation and Covariance Part 2 00:00:00
7.8 Mapping custom functions 00:00:00
7.9 Graphing percent change of income groups 00:00:00
7.10 Buffering basics 00:00:00
7.11 Buffering Into And Out Of Hdf5 00:00:00
7.12 Section Conclusion 00:00:00
8: Working With Databases 
8.1 Section Introduction 00:00:00
8.2 Writing to reading from database into a data frame 00:00:00
8.3 Resampling data and preparing graph 00:00:00
8.4 Finishing Manipulation And Graph 00:00:00
8.5 Section and course Conclusion 00:00:00

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