[{"data":1,"prerenderedAt":37},["ShallowReactive",2],{"training:fundamentals-of-data-science-egitimi:en":3},{"id":4,"title":5,"slug":6,"summary":7,"cover_image_url":8,"category":9,"duration_hours":15,"level":16,"module_count":17,"description":18,"outline":19,"faq":20,"updated_at":36},93,"Fundamentals of Data Science Training","fundamentals-of-data-science-egitimi","Data science has become a strategic discipline that fundamentally transforms companies' decision-making processes today, converting raw data into meaningful insights and competitive advantage. From e-commerce to banking, from manufacturing to healthcare, statistical analysis, machine learning, and data visualization techniques are actively used to understand customer behavior, improve operational efficiency, anticipate risks, and optimize marketing strategies. Organizations capable of data-driven decision-making are now leaving intuition-based management approaches behind, enabling them to take faster, more accurate, and more sustainable steps in the market.\n\nThe Fundamentals of Data Science Training equips participants with the core building blocks of the data science discipline—data collection, cleaning, exploratory analysis, statistical inference, and fundamental machine learning concepts—through hands-on examples. By the end of this training, participants gain the ability to make sense of the data within their own departments, ask the right questions, and develop data-driven solution proposals. From a corporate perspective, this training strengthens cross-departmental data-driven collaboration by building a data-literate workforce, increases the speed and accuracy of decision-making processes, and paves the way for data science to become an embedded cultural competency within the organization.","https:\u002F\u002Facademyclub.b-cdn.net\u002Ftrainings\u002F65370543e2e1432ea8588e49b5470c89.webp",{"id":10,"name":11,"slug":12,"icon":13,"order":14},5,"Data & Analytics","veri-analitik","pi-chart-bar",4,null,"",0,"\u003Ch3>Training Content and Sample Curriculum\u003C\u002Fh3>\u003Cp>Throughout the training, participants gain knowledge and skills spanning a wide range from fundamental data science concepts to applied analysis techniques. A sample curriculum is as follows:\u003C\u002Fp>\u003Cul>\u003Cli>Introduction to Data Science and the Ecosystem: The differences between data science, data engineering, and machine learning, current industry use cases, and career paths are covered.\u003C\u002Fli>\u003Cli>Data Collection and Preprocessing: Missing data management, outlier detection, data cleaning, and transformation techniques are explained through hands-on examples.\u003C\u002Fli>\u003Cli>Exploratory Data Analysis (EDA): Summary statistics extraction, correlation analysis, and visualization techniques (Matplotlib, Seaborn) are applied on datasets using Python\u002FPandas.\u003C\u002Fli>\u003Cli>Fundamental Statistics and Probability: Concepts such as mean, variance, distributions, hypothesis testing, and confidence intervals are reinforced with real business examples.\u003C\u002Fli>\u003Cli>Introduction to Machine Learning: A foundational introduction is provided to supervised and unsupervised learning concepts, along with regression and classification algorithms (Linear Regression, Decision Tree, K-Means).\u003C\u002Fli>\u003Cli>Model Evaluation and Interpretation: How to measure model performance using metrics such as accuracy, precision, and recall, and how to translate these into business outcomes, is covered.\u003C\u002Fli>\u003Cli>End-to-End Case Study: Participants experience the entire process hands-on, from problem definition to solution presentation, working on a real or near-real dataset.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>The curriculum for all our training programs is customized to suit your organization; please contact us for your specific needs. The information provided here has been prepared as a sample based on our previous training engagements.\u003C\u002Fp>\u003Ch3>Target Audience\u003C\u002Fh3>\u003Cul>\u003Cli>Business analysts and data analysts\u003C\u002Fli>\u003Cli>Software developers and software engineers\u003C\u002Fli>\u003Cli>Product managers and project managers\u003C\u002Fli>\u003Cli>Marketing and sales teams\u003C\u002Fli>\u003Cli>Finance and risk management professionals\u003C\u002Fli>\u003Cli>Any professional looking to transition into data science\u003C\u002Fli>\u003C\u002Ful>\u003Ch3>Organizational Benefits\u003C\u002Fh3>\u003Cul>\u003Cli>Strengthens a data-driven decision-making culture by increasing employees' data literacy\u003C\u002Fli>\u003Cli>Facilitates cross-departmental data sharing and collaboration\u003C\u002Fli>\u003Cli>Builds the capability to identify inefficiencies in operational processes through a data-driven approach\u003C\u002Fli>\u003Cli>Improves the accuracy of marketing and sales strategies by enabling better analysis of customer behavior\u003C\u002Fli>\u003Cli>Reduces external consulting costs by enabling simple data analysis projects to be carried out with internal resources\u003C\u002Fli>\u003Cli>Establishes a competency foundation within the organization that paves the way for advanced data science and artificial intelligence projects\u003C\u002Fli>\u003C\u002Ful>\u003Cp>Data is no longer just the responsibility of technical teams—it concerns everyone involved in the decision-making process. With the Fundamentals of Data Science Training, equip your team with the skills to understand, question, and translate data into business outcomes, bringing your organization one step closer to a data-driven future. As Academy Club, we are delighted to accompany you on this transformation journey — contact us to enroll in our training program.\u003C\u002Fp>",[],[21,24,27,30,33],{"answer":22,"question":23},"In this training, we cover introduction to data science and its ecosystem, data collection and preprocessing, exploratory data analysis (EDA), fundamental statistics and probability, introduction to machine learning, and model evaluation and interpretation. Participants also experience the entire process hands-on through an end-to-end case study, from problem definition to solution presentation. In exploratory analysis, practical applications are conducted using tools such as Python\u002FPandas, Matplotlib, and Seaborn.","What topics are covered in the Fundamentals of Data Science training?",{"answer":25,"question":26},"This training is suitable for business analysts and data analysts, software developers and software engineers, and product and project managers. It is also suitable for marketing and sales teams, finance and risk management professionals, and all professionals looking to transition into data science.","Who is the Fundamentals of Data Science training suitable for?",{"answer":28,"question":29},"The training strengthens a data-driven decision-making culture by improving employees' data literacy and facilitates cross-departmental data sharing and collaboration. It equips participants with the ability to identify operational inefficiencies through data, and enhances the accuracy of marketing and sales strategies by analyzing customer behavior. It also enables simple data analysis projects to be carried out with internal resources, reducing external consulting costs, and builds a competency foundation that paves the way for advanced data science and artificial intelligence projects.","What benefits does the Fundamentals of Data Science training provide to an organization?",{"answer":31,"question":32},"By the end of the training, participants gain the ability to make sense of data within their own departments, ask the right questions, and develop data-driven solution proposals. They learn data collection, cleaning, exploratory analysis, statistical inference, and fundamental machine learning concepts through hands-on examples.","What do participants gain by the end of the Fundamentals of Data Science training?",{"answer":34,"question":35},"Data science has become a strategic discipline that transforms raw data into meaningful insights and competitive advantage. From e-commerce to banking, manufacturing to healthcare, statistical analysis, machine learning, and data visualization techniques are used across every sector to understand customer behavior, improve operational efficiency, predict risks, and optimize marketing strategies. Organizations capable of data-driven decision-making can move faster, more accurately, and more sustainably in the market, leaving intuition-based management approaches behind.","Why is data science important for organizations?","2026-10-12T14:18:56.007644+03:00",1791823652116]