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How to Choose the Best Data Science Program

Pickl AI

Continuous Learning and Growth The field of Data Science is constantly evolving with new tools and technologies. Enrolling in a Data Science course keeps you updated on the latest advancements, such as machine learning algorithms and data visualisation techniques. billion by 2029, growing at a CAGR of 7.66%.

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Challenges of Big Data

Pickl AI

Dealing with a large volume of structured and unstructured data requires meticulous work and precision. Data scientists and Big Data analytics work rigorously to derive useful insights. Big Data has many benefits, as it improves decision-making, develops new products and reduce costs. What is Big Data?

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Data Science Journey Walkthrough – From Beginner to Expert

Smart Data Collective

Basics of Machine Learning. Machine learning is the science of building models automatically. In conventional programming, the programmer understands the business needs, data, and writes the logic. Whereas in machine learning, the algorithm understands the data and creates the logic.

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How can Financial Analysts start leveraging data skills?

Pickl AI

Financial Analysts can leverage tools like Tableau, Power BI, or Excel to create visually compelling data representations, enabling stakeholders to grasp key insights at a glance. Harnessing Big Data and Machine Learning The proliferation of big data has revolutionized how Financial Analysts approach data analysis.

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How to become a Statistician without a Degree?

Pickl AI

Clean and preprocess data to ensure its quality and reliability. Statistical Analysis: Apply statistical techniques to analyse data, including descriptive statistics, hypothesis testing, regression analysis, and machine learning algorithms. This can be a valuable asset when applying for jobs or graduate programs.

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An Analysis of the Loss Functions in Keras CV Tutorials

Heartbeat

Keras is popular high-level API machine learning framework in python that was created by Google. I decided that aggregating this data would give me a rough idea about what loss functions were commonly being used to solve the different problems. Glad you’re able to come on this journey! Innovation and academia go hand-in-hand.