What is Data Analytics
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Starting from 19th April
09:00 am – 1:00 pm Weekends
Fully Interactive Classroom Training
- 90 Hours Online Classroom Sessions
- 11 Module 04 Projects 5 MCQ Test
- 6 Months Complete Access
- Access on Mobile and laptop
- Certificate of completion
65,000 Students Enrolled
What we will learn in data analytics course Gurgaon in palin
- Connection, Navigation, Data Sources
- Data Extraction, Data Prep
- Data Pre-Processing
- Exploratory Data Analytics
- Data Wrangling
- Data Cleansing
- Data Munging
- Statistics For Business Analytics
- Data Analytics
- Machine Learning
- Data Visualization
- Python for everyone
What all topics we will cover in Data Analytics Course in Palin Analytics
Dive deep into the world of data analytics with a well-rounded curriculum covering statistical analysis, data visualization. Learn to make informed decisions and drive business strategies based on data-driven insights.
In this course we will learn python programming, statistics and analytics used for data analytics. We will learn data wrangling, data cleansing as well as data visualization using popular Python libraries like Numpy, Pandas, Matplotlib, and seaborn. In this course you will get to learn apply exploratory data analytics the essential part of data analytics.
By the end of this you will be able to extract, read and write data from csv files, data cleansing, data manipulation, data visualization, run inferential statistics, understand the business problems.What is the eligibility to Pursue Data Analytics Course in Palin Analytics Gurgaon
Data Science is meant for all and everyone should go for this, learn to play with data and grasping required skills isn’t just valuable, its essential now. Does not matter from which field you – economics, computer science, chemical, electrical, are statistics, mathematics, operations you will have to learn this.
Want to discuss your roadmap to become a Data Analyst?
Are you interested in pursuing a career as a data analyst, it’s essential to create a roadmap that outlines the key steps and milestones along the way. Join our data analytics course gurgaon for an inspiring conversation where we will deep dive into your own journey and discuss the clear cut roadmap to become a data analyst. Let’s start the journey to be a data analytics in the exciting world of analytics together!
Advantages
Countless Batch Access
Industry Expret Trainers
Shareable Certificate
Learn from anywhere
Career Transition Guidance
Real-Time Projects
Industry Endorsed Curriculum
Interview Preparation Techniques
Class recordings
Course Mentor
Vishal
- 10 + Batches
- 4.8 Star Rating
- 859 Students Trained
- 450+ Successfully Placed
Hi I am Vishal.
I hope this message finds you well. I’m thrilled to welcome you to Data Analytics Course our upcoming adventure in mastering the art of analytics. As your trainer, I’m genuinely excited about the journey we are about to embark on together.
What to Expect:
Over the 6 months of our course, we’ll be deep dive into the depth of analytics. From the fundamentals to advanced concepts, you can expect a dynamic and engaging learning experience. My goal is not only to impart knowledge but to guide you in applying these skills to real-world scenarios.
what is Course Content of data Analytics Course in Palin Analytics Gurgaon
Module 1: Introduction to Excel for Analytics
Overview of Excel as an analytics tool
Importance of Excel in data analysis
Excel interface and basic navigation
Module 2: Essential Excel Functions for Data Analysis
Understanding and using basic functions (SUM, AVERAGE, COUNT)
Working with mathematical and statistical functions
Text functions for data cleaning and manipulation
Logical functions (IF, AND, OR)
Module 3: Data Import and Cleaning in Excel
Importing data from various sources (CSV, Excel, databases)
Data cleaning techniques and best practices
Handling missing data
Module 4: Data Visualization in Excel
Creating basic charts (bar charts, line charts, pie charts)
Formatting and customizing charts
Using sparklines for trend analysis
Module 5: PivotTables and PivotCharts
Introduction to PivotTables
Creating PivotTables for data summarization
Building PivotCharts for visual analysis
Module 6: Advanced Excel Functions for Analytics
VLOOKUP and HLOOKUP for data retrieval
INDEX and MATCH functions
Advanced IF statements and nested functions
Using array formulas
Module 7: Data Analysis with Excel Tables
Introduction to Excel Tables
Sorting and filtering data in tables
Using structured references
Module 1: Introduction to Databases and SQL
Understanding Databases
Definition and types of databases
Relational databases
Non-relational databases
Introduction to SQL
What is SQL?
SQL history and evolution
Importance of SQL in the industry
Module 2: Basic SQL Commands
SELECT statement
Retrieving data from a single table
Filtering data using WHERE clause
Sorting data using ORDER BY clause
INSERT, UPDATE, DELETE statements
Adding, modifying, and deleting data
Data integrity considerations
Module 3: Data Types and Operators
Common SQL data types
Working with text, numeric, and date data
Arithmetic and comparison operators
Module 4: Advanced Querying
JOIN operations
INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN
Cross join and self-join
Subqueries
Nested queries
Correlated subqueries
Set operations
UNION, INTERSECT, EXCEPT
Module 5: Data Filtering and Aggregation
GROUP BY clause
HAVING clause
Aggregate functions (SUM, AVG, COUNT, MIN, MAX)
Module 6: Data Modification
Transactions
COMMIT and ROLLBACK statements
Constraints (PRIMARY KEY, FOREIGN KEY, UNIQUE, CHECK)
Module 7: Views and Indexes
Creating and managing views
Indexes and their impact on performance
Module 8: Stored Procedures and Functions
Creating stored procedures
Input and output parameters
User-defined functions
Module 9: Database Security
User roles and permissions
GRANT and REVOKE statements
Securing sensitive data
Module 10: Best Practices and Optimization
Writing efficient queries
Indexing strategies
Query optimization techniques
Module 11: Introduction to NoSQL Databases
Overview of NoSQL databases
Contrasting SQL and NoSQL
Module 12: Case Studies and Real-world Applications
Analyzing real-world scenarios
Building practical solutions with SQL
Introduction to Programming
Basics of programming logic
Understanding algorithms and flowcharts
Overview of Python as a programming language
Setting Up Python Environment
Installing Python
Working with Python IDEs
(Integrated Development Environments)
Writing and executing the first Python script
Python Basics
Variables and data types
Basic operations (arithmetic, comparison, logical)
Input and output (print, input)
Control Flow
Conditional statements (if, elif, else)
Loops (for, while)
Break and continue statements
Functions in Python
Defining functions
Parameters and return values
Scope and lifetime of variables
Lists and Tuples
Creating and manipulating lists
Slicing and indexing
Working with tuples
Dictionaries and Sets
Understanding dictionaries
Operations on sets
Use cases for dictionaries and sets
Module 4: File Handling
Reading and Writing Files
Opening and closing files
Reading from and writing to files
Working with different file formats (text, CSV)
Module 5: Error Handling and Modules
Error Handling
Introduction to exceptions
Try, except, finally blocks
Handling different types of errors
Python Libraries for Data Analytics
NumPy for numerical operations
Pandas for data manipulation and analysis
Matplotlib and Seaborn for data visualization
Data Cleaning and Preprocessing
Handling missing data
Data imputation techniques
Data normalization and standardization
Exploratory Data Analysis (EDA)
Descriptive Statistics
Measures of central tendency and dispersion
Skewness and kurtosis
Correlation and covariance
Module 1: Foundations of Statistics
Overview of StatisticsDefinition and scope of statistics
Descriptive vs. inferential statistics
Data Types and Measurement Scales
Categorical vs. numerical data
Nominal, ordinal, interval, and ratio scales
Descriptive Statistics
Measures of central tendency (mean, median, mode)
Measures of variability (range, variance, standard deviation)
Module 2: Probability Theory
Introduction to Probability
Basic probability concepts
Probability rules and laws
Probability Distributions
Discrete and continuous distributions
Normal distribution and its properties
Sampling Distributions
Central Limit Theorem
Standard error and confidence intervals
Module 3: Inferential Statistics
Hypothesis Testing
Formulating hypotheses
Type I and Type II errors
Parametric Tests
t-tests for means
Analysis of variance (ANOVA)
Non-parametric Tests
Mann-Whitney U test
Kruskal-Wallis test
Module 4: Correlation and Regression
Correlation Analysis
Pearson correlation coefficient
Spearman rank correlation
Regression Analysis
Simple linear regression
Multiple linear regression
Module 5: Bayesian Statistics
Bayesian Concepts
Bayes’ Theorem
Prior, likelihood, and posterior probabilities
Bayesian Inference
Bayesian hypothesis testing
Bayesian modeling
Module 1: Introduction to Tableau
Overview of Tableau
Understanding the Tableau interface
Connecting to data sources
Data source considerations and best practices
Module 2: Data Preparation and Cleaning in Tableau
Importing and cleaning data
Managing metadata
Joins and relationships
Data blending
Module 3: Basic Visualization Techniques
Creating basic charts (bar charts, line charts, scatter plots)
Building maps and geographic visualizations
Using size and color in visualizations
Dual-axis charts and combo charts
Module 4: Intermediate Visualization Techniques
Working with calculated fields
Building hierarchies
Creating sets and groups
Trend lines and reference lines
Module 5: Advanced Visualization Techniques
Advanced chart types (treemaps, heat maps, box plots)
Dashboard design principles
Storytelling with data
Interactive dashboards and actions
What Our Students Say About Us
Data Analytcs Demo classes in Palin Analytics Gurgaon
We are dedicated to empowering professionals as well as freshers with the skills and knowledge which is needed to upgrade in the field of Data Science. Whether you’re a beginner or a professional, our structured training programs are well designed to handle all levels of expertise.
Are you ready to explore your Data Science adventure? Watch a live recorded demo video now and discover the endless possibilities way of teaching, way of handling queries. Awaiting for you at Palin Analytics!
FAQ's
Data Science is an art of making data driven decisions. To make that data driven decision it uses scientific methods, processes, algorithms to extract knowledge and insights from data. Data science is related to data mining, Data Wrangling, machine learning and data visualization.
Data Science includes different processes like data gathering, data wrangling, data preprocessing, statistics, data visualization, machine learning. The mandate steps are Data preprocessing -> Data Visualization -> Exploratory Data Analysis ->Machine Learning -> Predictive Analysis
Along with the high quality training you will get a chance to work on real time projects as well, with a proven record of high placement support. We Provide one of the best online data science course.
Its Live interactive training, Ask your quesries on the go, no need to wait for doubt clearing.
you will have access to all the recordings, you can go through the recording as many times as you want.
During the training and after as well we will be on the same slack channel, where trainer and admin team will share study material, data, project, assignment.
Data analytics is the process of analyzing, interpreting, and gaining insights from data. It involves the use of statistical and computational methods to discover patterns, trends, and relationships in data sets.
Data analytics involves a variety of techniques, such as data mining, machine learning, and data visualization. Data mining is the process of discovering patterns and relationships in large data sets, while machine learning is a type of artificial intelligence that enables computer systems to learn from data and improve their performance over time. Data visualization is the process of presenting data in a visual format, such as charts and graphs, to help people understand complex data sets.
The goal of data analytics is to turn data into insights that can be used to make informed decisions. This can involve identifying opportunities for business growth, improving operational efficiency, or predicting future trends and outcomes. Data analytics is used in many industries, including finance, healthcare, marketing, and government, to name a few.
In summary, data analytics is the process of analyzing data to gain insights and make informed decisions. It involves a range of techniques and tools to extract valuable information from data sets.
There are many companies that offer internships in data analytics. Some of the well-known companies that provide internships in data analytics are:
Google: Google offers data analytics internships where you get to work on real-world data analysis projects and gain hands-on experience.
Microsoft: Microsoft provides internships in data analytics where you can learn about big data and machine learning.
Amazon: Amazon offers data analytics internships where you can learn how to analyze large datasets and use data to make business decisions.
IBM: IBM provides internships in data analytics where you can work on real-world projects and learn about data visualization, machine learning, and predictive modeling.
Deloitte: Deloitte offers internships in data analytics where you can gain experience in areas such as data analytics strategy, data governance, and data management.
PwC: PwC provides internships in data analytics where you can learn how to analyze data to identify trends, insights, and opportunities.
Accenture: Accenture offers internships in data analytics where you can work on projects related to data analytics, data management, and data visualization.
Facebook: Facebook provides internships in data analytics where you can gain experience in areas such as data modeling, data visualization, and data analysis.
These are just a few examples of companies that provide internships in data analytics. You can also search for internships in data analytics on job boards, company websites, and LinkedIn.
SQL (Structured Query Language) is a popular language used for managing and manipulating relational databases. The difficulty of learning SQL depends on your previous experience with programming, databases, and the complexity of the queries you want to create. Here are a few factors that can affect the difficulty of learning SQL:
Prior programming experience: If you have experience with other programming languages, you may find it easier to learn SQL as it shares some similarities with other languages. However, if you are new to programming, it may take you longer to grasp the concepts.
Familiarity with databases: If you are familiar with databases and data modeling concepts, you may find it easier to understand SQL queries. However, if you are new to databases, you may need to spend some time learning the basics.
Complexity of queries: SQL queries can range from simple SELECT statements to complex joins, subqueries, and window functions. The complexity of the queries you want to create can affect how difficult it is to learn SQL.
Overall, SQL is considered to be one of the easier programming languages to learn. It has a straightforward syntax and many resources available for learning, such as online courses, tutorials, and documentation. With some dedication and practice, most people can learn the basics of SQL in a relatively short amount of time.
you can write your questions at info@palin.co.in we will address your questions there.