Introduction to Data Analytics  

Analyzing data in order to extract relevant information, reach conclusions, and assist decision-making is known as data analytics. The training course "Introduction to Data Analytics" gives a general overview of the subject and discusses the key ideas, methods, and equipment involved in working with data. The fundamentals of statistics, data visualisation, and machine learning techniques are covered throughout the course. Additionally, participants will learn how to do data analysis using Python, SQL, and R. Participants will leave the course with a solid grasp of how to use data analytics methods to solve real-world issues and get insights from data.

• Basic statistics and probability theory
• Data collection and cleaning
• Data manipulation and exploration using tools such as Python or R
• Exploratory data analysis and visualisations using tools such as matplotlib, seaborn, ggplot
• Predictive modelling techniques such as linear regression, logistic regression, decision trees, and Random Forest
• Data storytelling and communication of insights
• Working with large datasets and big data technologies such as Hadoop, Spark, and NoSQL databases.

Syllabus

1. Introduction to Data Analytics:
   • Overview of Data Analytics and its applications
   • Types of data and data sources
   • The role of a Data Analyst
2. Data Wrangling:
   • Cleaning and preprocessing data
   • Handling missing values and outliers
   • Data Transformation
3. Exploratory Data Analysis:
   • Understanding the structure of data
   • Univariate and bivariate analysis
   • Visualisation techniques
4. Predictive Modelling:
   • Linear Regression
   • Logistic Regression
   • Decision Trees
   • Random Forests
5. Big Data Analytics:
   • Overview of Big Data
   • Handling and processing large data sets
   • Distributed computing frameworks
6. Data Visualization and Communication:
   • Creating meaningful visualisations
   • Data storytelling
   • Communicating insights and results to stakeholders
7. Machine Learning:
   • Supervised and unsupervised learning algorithms
   • Clustering and classification algorithms
   • Training and testing models
8. Tools and Technologies:
   • Overview of Data Analytics tools and technologies
   • Hands-on experience with programming languages such as Python and R
   • Data visualisation tools such as Tableau and Power BI.

Reason to join techspirals:

1. Data Detective Become a data detective by learning to glean insightful information from large volumes of data.
2. Future-Proof your Career:By learning in-demand data analytics skills, you may put yourself in a successful position for the future.
3. Power Up your Problem-Solving: Learn to use data-driven solutions to solve complicated business challenges, which will make you a valuable addition to any organisation.
4. Storyteller with Data: Learn to communicate insights to stakeholders via visually attractive and engaging data presentation.
5. Explore the World of Big Data: Discover hidden patterns and trends by examining and analysing large collections of real-world data using your hands-on expertise
6. Journey with Experts: Join a group of like-minded people and learn from seasoned data analytics experts to realise your full potential.