Learn Data Analytics from the Best Tutors
Search in
Answered on 20 Aug Learn Data Analytics
Sana Begum
My teaching experience 12 years
Answered on 20 Aug Learn Data Analytics
Sana Begum
My teaching experience 12 years
Answered on 20 Aug Learn Data Analytics
Sana Begum
My teaching experience 12 years
Learn Data Analytics from the Best Tutors
Answered on 08 Aug Learn Data Analytics
Gerryson Mehta
Data Analyst with 10 years of experience in Fintech, Product ,and IT Services
No, data science and data analytics are not the same:
1. **Data Science:**
- **Focus:** Building predictive models and algorithms.
- **Skills:** Programming, machine learning, advanced statistical methods.
- **Goal:** Create new tools and techniques for understanding and predicting trends.
2. **Data Analytics:**
- **Focus:** Analyzing historical and current data to find insights.
- **Skills:** Statistical analysis, data visualization, reporting.
- **Goal:** Interpret data to support decision-making and understand patterns.
### Summary:
- **Data Science** involves creating models and algorithms for predictions.
- **Data Analytics** focuses on analyzing and interpreting data for insights.
Answered on 08 Aug Learn Data Analytics
Gerryson Mehta
Data Analyst with 10 years of experience in Fintech, Product ,and IT Services
### Current Technologies in Data Analytics:
1. **Big Data Technologies:** Tools like Hadoop and Spark handle large-scale data processing and analysis.
2. **Data Visualization Tools:** Software like Tableau and Power BI create interactive dashboards and visualizations.
3. **Data Warehousing:** Solutions like Amazon Redshift and Snowflake store and manage large volumes of data.
4. **Machine Learning:** Libraries and frameworks like TensorFlow and Scikit-learn for predictive modeling.
5. **SQL Databases:** Systems like MySQL and PostgreSQL for querying and managing relational data.
6. **Cloud Analytics:** Platforms like Google Cloud and Azure provide scalable analytics services.
7. **Data Integration Tools:** Tools like Talend and Apache Nifi integrate data from various sources.
### Summary:
These technologies help collect, process, visualize, and analyze data efficiently.
Answered on 08 Aug Learn Data Analytics
Gerryson Mehta
Data Analyst with 10 years of experience in Fintech, Product ,and IT Services
Big data analytics involves analyzing vast amounts of complex data to uncover patterns, correlations, and trends. It typically includes:
1. **Data Collection:** Gathering data from various sources, such as social media, sensors, and transactions.
2. **Data Processing:** Using technologies like Hadoop and Spark to handle large datasets.
3. **Data Analysis:** Applying statistical methods, machine learning, and algorithms to find insights.
4. **Data Visualization:** Creating charts and dashboards to represent findings clearly.
### Summary:
Big data analytics helps organizations make sense of massive amounts of data to drive decisions and uncover valuable insights.
Learn Data Analytics from the Best Tutors
Answered on 08 Aug Learn Data Analytics
Gerryson Mehta
Data Analyst with 10 years of experience in Fintech, Product ,and IT Services
### Differences Between Analytics and Analysis:
1. **Data Analysis:**
- **Focus:** Examines data to understand past and current trends.
- **Process:** Includes data cleaning, summarizing, and interpreting to uncover insights.
- **Outcome:** Provides specific answers or insights from data.
2. **Data Analytics:**
- **Focus:** Uses data analysis techniques along with advanced tools to support decision-making.
- **Process:** Involves not only analyzing data but also applying algorithms, predictive modeling, and visualization.
- **Outcome:** Provides comprehensive insights and recommendations to inform business strategies and decisions.
### Summary:
- **Analysis** is the act of examining data.
- **Analytics** is a broader process that includes analysis, along with advanced techniques for deeper insights and decision support.
Answered on 08 Aug Learn Data Analytics
Gerryson Mehta
Data Analyst with 10 years of experience in Fintech, Product ,and IT Services
### Requirements for Big Data Analytics:
1. **Data Sources:** Large volumes of data from various sources like social media, sensors, and databases.
2. **Data Storage:** Scalable storage solutions such as Hadoop Distributed File System (HDFS) or cloud storage like AWS S3.
3. **Data Processing Tools:** Technologies like Apache Spark and Hadoop to process and manage large datasets.
4. **Data Analysis Tools:** Software for statistical analysis and machine learning, such as R, Python, and SQL.
5. **Data Visualization Tools:** Platforms like Tableau, Power BI, or custom dashboards to visualize insights.
6. **Infrastructure:** High-performance computing resources and scalable cloud services.
### Summary:
Big data analytics requires robust storage, processing, and analysis tools to handle and make sense of large volumes of data.
Answered on 20 Aug Learn Data Analytics
Raajendra Prasath
Basic to Advanced Level Digital Marketing Expert Trainer with Over 10+ Years of IT Experience
Data analytics training involves teaching individuals how to collect, process, and analyze data to extract valuable insights that can be used to make informed decisions. This training typically covers various techniques and tools for handling data, such as statistical analysis, data visualization, data mining, and machine learning.
Participants learn how to work with different types of data, use software like Excel, SQL, Python, or R, and apply analytical methods to solve real-world problems. The goal of data analytics training is to equip learners with the skills needed to interpret complex data sets, identify trends, and contribute to data-driven decision-making in various industries.
read lessLearn Data Analytics from the Best Tutors
Answered on 20 Aug Learn Data Analytics
Raajendra Prasath
Basic to Advanced Level Digital Marketing Expert Trainer with Over 10+ Years of IT Experience
Python is widely used in data analytics because of its simplicity, versatility, and the extensive range of libraries and tools it offers. Here’s how Python is useful in data analytics:
Data Manipulation: Python has powerful libraries like Pandas and NumPy that make it easy to manipulate and clean large datasets. These tools allow analysts to filter, sort, and transform data efficiently.
Data Visualization: Python provides several libraries, such as Matplotlib, Seaborn, and Plotly, which help in creating detailed and informative visualizations. These visualizations help analysts to understand data trends and patterns easily.
Statistical Analysis: Python’s libraries like SciPy and Statsmodels enable users to perform complex statistical analyses, which are essential for interpreting data and making data-driven decisions.
Automation: Python can automate repetitive tasks, such as data extraction, transformation, and loading (ETL), which saves time and reduces the possibility of errors.
Machine Learning: Python is a preferred language for implementing machine learning models using libraries like Scikit-Learn, TensorFlow, and Keras. These models help in making predictions and discovering insights from data.
Integration: Python easily integrates with other tools and systems, making it a versatile choice for data analytics in various environments.
Overall, Python's ease of use, combined with its powerful libraries, makes it a go-to language for data analytics professionals.
read lessUrbanPro.com helps you to connect with the best Data Analytics classes in India. Post Your Requirement today and get connected.
Ask a Question
The best tutors for Data Analytics Classes are on UrbanPro
The best Tutors for Data Analytics Classes are on UrbanPro