Professional Certificate in Data Science Fundamentals: Analysis Skills

Name: Professional Certificate in Data Science Fundamentals: Analysis Skills
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The Professional Certificate in Data Science Fundamentals: Analysis Skills equips learners with essential data analysis skills demanded by today's industry. This course emphasizes the importance of data-driven decision-making, enabling professionals to extract valuable insights from data and communicate them effectively.

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AboutThisCourse

By learning fundamental data science concepts, statistical analysis, and data visualization techniques, learners can advance their careers in various fields, including business, finance, healthcare, and technology. The course covers key topics like data collection, cleaning, and preprocessing, exploratory data analysis, and data storytelling. Industry-leading tools and technologies, such as Python, Pandas, NumPy, Matplotlib, and Seaborn, are utilized throughout the course. Upon completion, learners will have a solid foundation in data science, enabling them to tackle real-world challenges, drive innovation, and stay competitive in the ever-evolving data-driven economy.

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CourseDetails

• Data Collection: Understanding data sources, data types, and data collection methods. Introduction to data quality and data validation techniques.
• Data Cleaning: Techniques for cleaning and preparing data for analysis. Identifying and handling missing data, outliers, and inconsistencies.
• Data Analysis with Python: Introduction to Python programming for data analysis. Learning key libraries such as NumPy, Pandas, and Matplotlib.
• Data Analysis with R: Introduction to R programming for data analysis. Learning key packages such as dplyr, ggplot2, and tidyr.
• Statistical Analysis: Understanding key statistical concepts such as hypothesis testing, regression analysis, and probability distributions.
• Data Visualization: Techniques for creating effective and informative data visualizations. Learning best practices for chart selection, color choice, and data encoding.
• Data Storytelling: Communicating insights from data to stakeholders. Learning how to craft compelling narratives, create effective presentations, and communicate insights to non-technical audiences.
• Machine Learning Fundamentals: Introduction to machine learning concepts such as supervised and unsupervised learning, model evaluation, and model selection.
• Ethics in Data Science: Understanding ethical considerations in data science such as data privacy, data security, and algorithmic bias.

CareerPath

In the UK, data science fundamentals are in high demand with various roles experiencing significant growth. The above 3D pie chart illustrates the distribution of roles in this field, based on job market trends. *Data Analyst*: With 35% of the market share, Data Analysts are the most common role in the UK's data science scene. These professionals focus on extracting insights from data and presenting actionable results to stakeholders. *Data Scientist*: Accounting for 45% of the market, Data Scientists have a strong presence in the UK due to their expertise in designing and implementing algorithms for data analysis and predictive modeling. *Data Engineer*: Data Engineers represent 15% of the data science field, specializing in building and maintaining data infrastructure, ensuring data quality, and implementing data security measures. *Business Intelligence Analyst*: Making up the remaining 5%, Business Intelligence Analysts concentrate on analyzing data to provide insights into business operations, helping organizations make informed decisions. By observing these trends, aspiring data professionals can align their skillsets with market demands, ensuring a successful career path in data science fundamentals.

EntryRequirements

BasicUnderstandingSubject
ProficiencyEnglish
ComputerInternetAccess
BasicComputerSkills
DedicationCompleteCourse

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FastTrack GBP £140

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ThreeFourHoursPerWeek
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StandardMode GBP £90

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TwoThreeHoursPerWeek
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PROFESSIONAL CERTIFICATE IN DATA SCIENCE FUNDAMENTALS: ANALYSIS SKILLS

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London College of Foreign Trade (LCFT)

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05 May 2025

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