Pickl AI

NOVEMBER 6, 2023

Steps to Perform Data Visualization: Data visualization is the presentation of information and statistics using visual tools that include charts, graphs, and maps. Its goal is to create patterns in data, trends, and anomalies comprehensible to both data professionals and people without technical knowledge.

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Dataconomy

MAY 2, 2023

What do machine learning engineers do? They design, develop, and deploy the machine learning algorithms that power everything from self-driving cars to personalized recommendations. What do machine learning engineers do? Does a machine learning engineer do coding? They build the future.

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Pickl AI

MARCH 20, 2024

Aspiring Data Scientists must equip themselves with a diverse skill set encompassing technical expertise, analytical prowess, and domain knowledge. Whether you’re venturing into machine learning, predictive analytics, or data visualization, honing the following top Data Science skills is essential for success.

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Pickl AI

JULY 25, 2023

Proficient in programming languages like Python or R, data manipulation libraries like Pandas, and machine learning frameworks like TensorFlow and Scikit-learn, data scientists uncover patterns and trends through statistical analysis and data visualization. Big Data Technologies: Hadoop, Spark, etc.

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Pickl AI

JANUARY 15, 2025

Techniques such as descriptive statistics (mean, median, mode) allow data scientists to understand the dataset’s characteristics better, ensuring that the analysis is built on a solid foundation. Hypothesis Testing Hypothesis testing is a vital aspect of statistics that allows data scientists to validate assumptions about a dataset.

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Pickl AI

JULY 10, 2023

R is a popular programming language and environment widely used in the field of data science. It provides a comprehensive suite of tools, libraries, and packages specifically designed for statistical analysis, data manipulation, visualization, and machine learning.

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Pickl AI

AUGUST 21, 2023

Key Takeaways: Data Science is a multidisciplinary field bridging statistics, mathematics, and computer science to extract insights from data. The roadmap to becoming a Data Scientist involves mastering programming, statistics, machine learning, data visualization, and domain knowledge.

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Pickl AI

SEPTEMBER 4, 2023

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 includes randomization, control groups, and minimising bias.

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Pickl AI

AUGUST 9, 2024

Statistical Analysis Introducing statistical methods and techniques for analysing data, including hypothesis testing, regression analysis, and descriptive statistics. Students should gain a foundational understanding of statistics as it applies to data analytics. What are the Ethical Considerations in Big Data?

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Pickl AI

JUNE 19, 2024

Introduction to Data Science Courses Data Science courses come in various shapes and sizes. There are beginner-friendly programs focusing on foundational concepts, while more advanced courses delve into specialized areas like machine learning or natural language processing. Course Focus Data Science is a vast field.

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Pickl AI

JULY 28, 2023

Data Cleaning is a crucial step in any data analysis process, and it’s important to showcase your ability to handle messy data effectively. Data Visualization: Create compelling and informative Data Visualizations. Visual Appeal: Use clean and visually appealing Data Visualizations.

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Pickl AI

APRIL 26, 2024

This comprehensive blog outlines vital aspects of Data Analyst interviews, offering insights into technical, behavioural, and industry-specific questions. It covers essential topics such as SQL queries, data visualization, statistical analysis, machine learning concepts, and data manipulation techniques.

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Pickl AI

JULY 23, 2023

Examples of unstructured data include text files, images, audio, and video content. While unstructured data may seem chaotic, advancements in artificial intelligence and machine learning enable us to extract valuable insights from this data type. Examples of Descriptive Analytics: a. Key Features: i.

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Pickl AI

MAY 30, 2024

You will collect and clean data from multiple sources, ensuring it is suitable for analysis. You will perform Exploratory Data Analysis to uncover patterns and insights hidden within the data. Visualising data makes it easier to identify anomalies and understand distributions.

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Mlearning.ai

MAY 23, 2023

Read the full blog here —  [link] Data Science Interview Questions for Freshers 1. What is Data Science? Once the exploratory steps are completed, the cleansed data is subjected to various algorithms like predictive analysis, regression, text mining, recognition patterns, etc depending on the requirements. Explain it’s working.

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