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The Certified Data Science Practitioner (CDSP) course is a comprehensive training program designed to equip learners with the essential skills and knowledge required to tackle real-world business issues using data science. From initiating and formulating data science projects in Module 1 to effectively communicating results to stakeholders in Module 8, the course covers the full spectrum of data science workflow.
Learners will master the art of Extracting, Transforming, and Loading Data in Module 2, followed by Analyzing Data in Module 3 using various statistical techniques and visualizations. They will then dive into Designing a Machine Learning Approach in Module 4 and further develop specialized skills in Developing Classification Models, Regression Models, and Clustering Models in Modules 5, 6, and 7 respectively.
By the end of the course, participants will be able to deliver end-to-end data science solutions, from Finalizing a Data Science Project to implementing these solutions in a production environment, ensuring they are well-prepared to become proficient data science professionals.
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Classroom Training price is on request
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♱ Excluding VAT/GST
Classroom Training price is on request
You can request classroom training in any city on any date by Requesting More Information
To ensure a successful learning experience in the Certified Data Science Practitioner (CDSP) course, students should meet the following minimum prerequisites:
Please note, while prior experience in data science is not strictly required, a general understanding of the data analytics process will be beneficial. This course is designed to be accessible to those with a foundational skill set and a willingness to learn and engage with the material presented.
The Certified Data Science Practitioner (CDSP) course equips learners with practical skills to solve business problems using data science methodologies.
Target audience for the CDSP course includes:
The Certified Data Science Practitioner (CDSP) course is designed to equip learners with the skills to address business problems through data analytics, machine learning model development, and effective communication of results.
Extracting, Transforming, and Loading (ETL) data involves three critical steps used in data processing. First, data is extracted from various sources, which could be databases, spreadsheets, or other data repositories. Next, this data is transformed where necessary to fit operational needs, which may include cleansing, applying calculations, or reformatting it. Finally, the transformed data is loaded into a final target database, ready for analysis and decision-making. This process is essential for data science practitioners ensuring that data is accurate and organized for actionable insights.
Analyzing data involves reviewing information using methods ranging from basic data scrutiny to complex algorithms, in order to extract meaningful insights. This process helps businesses and organizations make informed decisions. Data science practitioners often use statistical techniques and machine learning tools to interpret, predict and visualize data trends. A certified data science practitioner possesses extensive training in these areas, ensuring competency in handling diverse data sets and extracting valuable conclusions efficiently. Effective data analysis is crucial for strategic planning and can significantly impact the success of various projects across industries.
Designing a Machine Learning approach involves creating models that allow computers to learn from and make decisions based on data. Initially, you define the problem, select the data relevant to the issue, and preprocess it to enhance quality. The next step is to choose an appropriate algorithm that suits your needs—whether it's for pattern recognition, predictive analysis, or another goal. Following this, you train the model using your chosen dataset, testing and tweaking it to improve accuracy. Finally, the model is evaluated and deployed to perform the designated task autonomously, continually learning and adapting from new data inputs.
Developing classification models is a process used in data science to categorize data into predefined classes or labels, helping certified data science practitioners make predictions or decisions. This involves collecting data, selecting relevant features, and choosing a suitable algorithm (like decision trees or neural networks). The model is trained on a portion of the data, learning to recognize patterns that determine the class of each input. After training, its performance is evaluated using a separate set of data, ensuring the model accurately and reliably predicts the correct category for new, unseen data.
Regression models are statistical tools used by data science practitioners to understand the relationship between a dependent variable and one or more independent variables. By analyzing data, these models help predict the outcome of the dependent variable based on the values of the independent variables. For example, a certified data science practitioner might use regression analysis to predict house prices (dependent variable) based on factors such as location, size, and age of the house (independent variables). This technique is essential in fields ranging from economics to biology, aiding in decision-making and forecasting.
Clustering models are a type of algorithm used in data science to group similar objects into sets, called clusters. The objective is to organize data in such a way that objects in the same cluster are more similar to each other than to those in other clusters. This technique is pivotal in various applications such as market segmentation, anomaly detection, and organizing large sets of data for further analysis. It helps certified data science practitioners make sense of complex data by identifying natural groupings and patterns, allowing for more targeted and effective decision-making.
Finalizing a Data Science Project involves several key steps to ensure the success and practical application of your findings. It starts with validating your data models against new data to ensure their accuracy and reliability. Then, you need to rigorously document your methodologies, results, and conclusions to facilitate stakeholder understanding and future audits. Finally, deploy the solution into the operational environment, and monitor its performance continuously, making adjustments as necessary. This phase is crucial for translating your data insights into actionable, impactful business outcomes, solidifying your role as a certified data science practitioner.
The Certified Data Science Practitioner (CDSP) course equips learners with practical skills to solve business problems using data science methodologies.
Target audience for the CDSP course includes:
The Certified Data Science Practitioner (CDSP) course is designed to equip learners with the skills to address business problems through data analytics, machine learning model development, and effective communication of results.
