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Data Analytics News
Medium
1h
198
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What is the Data Science Lifecycle? A Complete Guide
- The data science lifecycle is a framework that guides data science projects from conception to deployment.
- It involves defining the problem, collecting data from various sources, cleaning and preparing the data, conducting exploratory data analysis, building models, evaluating and deploying the models.
- Continuous monitoring and maintenance of the deployed models is also important, as well as effective communication of results to stakeholders.
- By following this structured approach, organizations can leverage data science to make informed decisions and optimize operations.
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Medium
2h
26
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Towards Explainable Analyses: Empowering Audiences with Data Storytelling
- Explainable AI (xAI) has gained traction as non-technical audiences seek transparency in highly technical products like ChatGPT.
- Data storytelling is a powerful tool for achieving transparent analyses and engaging with the broader public.
- Data storytelling involves crafting a narrative around data, using plain language, and employing visualizations to enhance understanding.
- Tailoring the narrative and visuals to the target audience is crucial for impactful data storytelling.
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Medium
2h
316
Empowering Data Agility: Azure Service Fabric in Lake House Architecture
- Azure Service Fabric serves as the backbone for deploying and managing the different parts of a unified data platform.
- Scalability and Resilience: Azure Service Fabric can handle large volumes of data, scale micro-services based on workload demands, and ensure uninterrupted access to critical data resources.
- Real-Time Data Processing: Azure Service Fabric enables the deployment of micro-services for real-time data analysis, allowing businesses to extract insights from streaming sources with minimal delay.
- Data Governance and Security: Azure Service Fabric offers robust security features, ensuring that data remains secure and compliant with regulations in Lake House architectures.
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Medium
10h
10
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The Transformative Power of Data Science: Unlocking Insights and Driving Innovation
- Data science is a multidisciplinary field that combines elements of statistics, mathematics, computer science, and domain-specific knowledge to extract valuable information from data.
- Applications of data science are wide-ranging, spanning industries such as healthcare, finance, e-commerce, and addressing global challenges.
- Data science has a profound impact on our lives, enabling organizations to make informed decisions, improve operational efficiency, and drive innovation.
- Challenges and ethical considerations, such as data privacy and algorithmic bias, must be addressed to ensure the responsible use of data science.
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Medium
11h
115
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A Capstone Journey into Healthcare and Data Science
- The dataset used for this project was sourced from Kaggle, containing drug performance metrics for 37 common conditions.
- The data was loaded using pandas and explored to understand its structure and identify missing values.
- A pipeline was created to automate the training process, integrating the preprocessor and model.
- The Drug Satisfaction Prediction App demonstrates the integration of data science and healthcare, providing real-world solutions and opportunities for further advancements.
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Hackernoon
16h
253
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How to Connect to Oracle, MySql and PostgreSQL Databases Using Python
- Step 1: Install the necessary libraries - pandas and sqlalchemy
- Step 2: Connect to the database using appropriate connection string
- Step 3: Query data using the read_sql function from pandas
- Example code provided for connecting to MySQL, PostgreSQL, and Oracle databases
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Pymnts
22h
175
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Customer-First Focus Unlocks Financial Technology Innovation
- Financial services account for 25% — or $1 for every $4 — spent in technology today.
- Using technology to implement the five C’s of data: Capture, Clean, Cache, Call, and Connect.
- Breaking down data silos and integrating various data sources is crucial.
- Delivering outstanding services and customer experiences using data insights.
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10 Likes
Medium
1d
116
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Data Analytics Skills You Required in 2024
- Data analytics skills are crucial in today's data-driven world.
- Mastery of statistical concepts is important for effective data interpretation.
- Proficiency in programming languages like Python, R, and SQL is indispensable.
- Skills in creating compelling visualizations and knowledge of machine learning techniques are valuable.
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7 Likes
Medium
1d
364
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Analyzing and Visualizing Apple’s Stock Data Over the Past 10 Years Using Power BI
- To analyze and visualize Apple's stock data over the past 10 years using Power BI, follow these steps:
- Access Yahoo Finance and search for 'AAPL' (Apple's stock symbol)
- Download the historical stock data as a CSV file
- Import the CSV file into Power BI and cleanse the data
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21 Likes
Medium
1d
169
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Hey! You Forget to Clean Your Data
- Data cleaning is the process of detecting, correcting or removing incorrect, corrupted, incomplete data in a dataset or table.
- Handling missing values is an important step in data cleaning to avoid bias and inaccurate conclusions.
- Deletion and imputation are common techniques used in data cleaning to handle missing values.
- Data cleaning also involves addressing data duplication and ensuring consistent data formats and types.
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Medium
1d
73
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SQL Resources that are worth the shot!
- Highly recommended SQL courses to enhance your skills:
- 1. Course offered by Coursera, tailored for data science enthusiasts. Link: https://lnkd.in/gucmWzZD
- 2. Udemy course: Go from SQL Beginner to Expert. Link: https://lnkd.in/gzgHSNMR
- 3. Course available on DataCamp, focused on SQL data manipulation. Link: https://lnkd.in/g_QjCwJ6
- 4. Analytic Techniques for Business Specialization by Duke University. Link: https://lnkd.in/gkctuhTq
- 5. Extensive SQL course by LearnSQL covering beginner to advanced topics. Link: https://lnkd.in/gf9Y6gFM
- 6. Zero to Hero course on Udemy, covering SQL basics to complex queries. Link: https://lnkd.in/gf9tV4Ef
- These courses cater to different learning needs and levels of SQL knowledge. Happy learning!
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Medium
1d
92
Unveiling the World of Matplotlib
- Matplotlib is a powerful library for data visualization in Python.
- Key terminologies in Matplotlib include Figure, Axes, Axis, Artist, Plot, Line2D, Markers, Color, Legend, Title, Labels, Ticks, Grid, Subplot, Figure Size, Aspect Ratio, Resolution, and Backend.
- Matplotlib offers a wide range of plot types and customization options, making it a versatile tool for creating engaging visual narratives.
- Whether you're a beginner or an expert in data science, Matplotlib empowers you to unleash your creativity and transform raw data into compelling visual stories.
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Analyticsinsight
1d
135
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10 Hottest Data Analytics Companies You Need to Know
- Palantir Technologies offers data integration and analytics platform for strategic decision-making.
- Snowflake revolutionizes data management and analysis with its cloud-based platform.
- Databricks provides a unified analytics platform built on Apache Spark for real-time insights.
- Alteryx offers a self-service analytics platform for data preparation and analysis.
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8 Likes
Medium
2d
385
Image Credit: Medium
Google Faces Changes in Leadership Amid Financial Loss.
- Google is facing changes in leadership amid financial loss.
- Sundar Pichai has been the CEO since 2015, leading Google through significant growth and expansion.
- However, investors are concerned about the lack of new innovative products and recent challenges.
- As a result, there is speculation about who will take over if Pichai steps down.
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Analyticsinsight
2d
250
Image Credit: Analyticsinsight
Highest Data Analytics Salaries in India
- Leading technology companies and IT services firms offer some of the highest salaries in the data analytics field in India.
- The financial services sector, including banking, insurance, and fintech companies, has a significant demand for data analytics talent.
- E-commerce and retail companies heavily rely on data analytics, offering competitive salaries to professionals in roles such as business intelligence analysts.
- The healthcare and pharmaceutical industries offer attractive salaries to data analytics professionals working in roles such as healthcare data scientists and clinical research analysts.
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