Data Analytics
The Data Analytics course equips learners with the skills to collect, analyze, and interpret data for better decision-making. It covers tools like Excel, SQL, Power BI, and Python to transform raw data into actionable insights for business growth.
Data Analytics Course Gurgaon : Unlock the power of data with exclusive Data Analytics course training by Palin Analytics Gurgaon. In an era where data is everything, its the driving force behind every business decisions, this course is designed to furnish you with the skills and knowledge which is required to extract meaningful insights from complex datasets.
What you'll learn
- Connection, Navigation, Data Sources
- Data Extraction, Data Prep
- Data Pre-Processing
- Exploratory Data Analytics
- Data Wrangling
- Data Cleansing
- Statistics For Business Analytics
- Data Munging
- 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 Expert Trainers
Shareable Certificate
Learn from anywhere
Career Transition Guidance
Real-Time Projects
Industry Experts
Interview Preparation
Class recordings
FAQ's
What is machine learning, and how does it differ from traditional programming?
Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on the development of algorithms and models that enable computers to learn and make predictions or decisions based on data. The primary distinction between machine learning and traditional programming lies in how they handle tasks and acquire knowledge:
Can you explain the main types of machine learning, such as supervised, unsupervised, and reinforcement learning?
Supervised learning involves training a model on a labeled dataset, where the input data is paired with corresponding output labels.
Unsupervised learning involves training a model on an unlabeled dataset where the algorithm tries to find patterns or structures in the data without explicit guidance on the output.
Reinforcement learning involves an agent interacting with an environment and learning to make decisions by receiving feedback in the form of rewards or penalties.
Why we choose Palin Analytics for Data Science training?
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.
Is this a Live Training or a recorded sessions?
Its Live interactive training, Ask your quesries on the go, no need to wait for doubt clearing.
What if i miss any session?
you will have access to all the recordings, you can go through the recording as many times as you want.
How will we get the access of Data to do practice?
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.
What is Data Analytics?
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.
which companies provides internships in data analytics?
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.
- 5 Sections
- 161 Lessons
- 10 Weeks
- Advance Excel18
- 1.1Introduction to Excel for Analytics Overview30 Minutes
- 1.2Importance of Excel in Data Analytics10 Minutes
- 1.3Excel interface and basic navigation10 Minutes
- 1.4Essential Excel Functions for Data Analytics
- 1.5Understanding and using basic functions (SUM, AVERAGE, COUNT)
- 1.6Working with mathematical and statistical functions
- 1.7Text functions for Data Cleaning and manipulation
- 1.8Logical Functions (IF, AND, OR)
- 1.9Data Import and Cleaning in Excel
- 1.10Importing Data from Various sources (CVS, Excel, Databases)
- 1.11Data Cleaning techniques and best practices Handling missing data
- 1.12Data Visualization in Excel Creating basic charts (bar charts, line charts, Pie charts)
- 1.13Introduction to PivotTables
- 1.14PivotTables and PivotCharts
- 1.15Creating PivotTables for Data Summarization
- 1.16Building PivotCharts for visual analysis
- 1.17Data Analysis with Excel tables
- 1.18Introduction to Excel Tables
- SQL35
- 2.1Introduction to Databases and SQL
- 2.2Understanding Databases45 Minutes
- 2.3Definition and types of databases45 Minutes
- 2.4Relational Databases and Non-relational Databases
- 2.5Introduction to SQL and What is SQL ?
- 2.6SQL History and Evaluation
- 2.7Importance of SQL in the industry
- 2.8Basic SQL Commands
- 2.9Filtering data using WHERE clause
- 2.10Retrieving data from a single table
- 2.11Advanced Querying
- 2.12Ata Modification
- 2.13COMMIT and ROLLBACK Statement
- 2.14Constraints (PRIMARY KEY, FOREIGN KEY, UNIQUE, CHECK)
- 2.15Aggregate Functions (SUM, AVG, COUNT, MIN, MAX)
- 2.16Stored Procedures and Functions
- 2.17Creation Stored Procedures
- 2.18Input and Output parameters
- 2.19User-Defined Functions
- 2.20Database Security
- 2.21User Roles and Permissions
- 2.22GRANT and REVOKE Statements
- 2.23Securing Sensitive Data
- 2.24Introduction to NoSQL Databases
- 2.25Overview of NoSQL Databases
- 2.26Best Practices and Optimization
- 2.27Writing Efficient Queries
- 2.28Query optimization techniques
- 2.29Case Studies and Real-World Applications
- 2.30Analyzing real-world Senarios
- 2.31Building practical solutions with SQL
- 2.32Views and Indexes
- 2.33Creating and managing views
- 2.34Indexes and their impact on performance
- 2.35Data Filtering and Aggregation
- Python50
- 3.1Introduction to Programming10 Minutes
- 3.2Basics of Programming Logic10 Minutes
- 3.3Understanding algorithms and flowcharts
- 3.4Overview of Python as a programming language
- 3.5Setting Up Python Environment
- 3.6Installing Python
- 3.7Working with Python IDEs
- 3.8(Integrated Development Environments)
- 3.9Writing and executing the first Python script
- 3.10Python Basics
- 3.11Variables and Data Types
- 3.12Basic operations (arithmetic, comparison, logical)
- 3.13Input and output (print, input)
- 3.14Control Flow
- 3.15Conditional statements (if, elseif, else)
- 3.16Loops (for, while)
- 3.17Break and continue statements
- 3.18Functions in Python
- 3.19Defining functions
- 3.20Parameters and return values
- 3.21Scope and lifetime of variables
- 3.22Lists and Tuples
- 3.23Creating and manipulating lists
- 3.24Slicing and indexing
- 3.25Working with tuples
- 3.26Dictionaries and Sets
- 3.27Understanding dictionaries
- 3.28Operations on sets
- 3.29Use cases for dictionaries and sets
- 3.30Reading and Writing Files
- 3.31Opening and closing files
- 3.32Reading from and writing to files
- 3.33Working with different file formats (text, CSV)
- 3.34Error Handling
- 3.35Introduction to exceptions
- 3.36Try, except, finally blocks
- 3.37Handling different types of errors
- 3.38Python Libraries for Data Analytics
- 3.39NumPy for numerical operations
- 3.40Pandas for data manipulation and analysis
- 3.41Matplotlib and Seaborn for data visualization
- 3.42Data Cleaning and Preprocessing
- 3.43Handling missing data
- 3.44Data imputation techniques
- 3.45Data normalization and standardization
- 3.46Exploratory Data Analysis (EDA)
- 3.47Descriptive Statistics
- 3.48Measures of central tendency and dispersion
- 3.49Skewness and kurtosis
- 3.50Correlation and covariance
- Statistics38
- 4.1Overview of Statistics Definition and scope of statistics10 Minutes
- 4.2Descriptive vs. inferential statistics15 Minutes
- 4.3Data Types and Measurement Scales
- 4.4Categorical vs. numerical data
- 4.5Nominal, ordinal, interval, and ratio scales
- 4.6Descriptive Statistics
- 4.7Measures of central tendency (mean, median, mode)
- 4.8Measures of variability (range, variance, standard deviation)
- 4.9Introduction to Probability
- 4.10Basic probability concepts
- 4.11Probability rules and laws
- 4.12Probability Distributions
- 4.13Discrete and continuous distributions
- 4.14Normal distribution and its properties
- 4.15Sampling Distributions
- 4.16Central Limit Theorem
- 4.17Standard error and confidence intervals
- 4.18Hypothesis Testing
- 4.19Formulating hypotheses
- 4.20Type I and Type II errors
- 4.21Parametric Tests
- 4.22t-tests for means
- 4.23Analysis of variance (ANOVA)
- 4.24Non-parametric Tests
- 4.25Mann-Whitney U test
- 4.26Kruskal-Wallis test
- 4.27Correlation Analysis
- 4.28Pearson correlation coefficient
- 4.29Spearman rank correlation
- 4.30Regression Analysis
- 4.31Simple linear regression
- 4.32Multiple linear regression
- 4.33Bayesian Concepts
- 4.34Bayes’ Theorem
- 4.35Prior, likelihood, and posterior probabilities
- 4.36Bayesian Inference
- 4.37Bayesian hypothesis testing
- 4.38Bayesian modeling
- Data Visualization20
- 5.1Overview of Tableau
- 5.2Understanding the Tableau interface
- 5.3Connecting to data sources
- 5.4Data source considerations and best practices
- 5.5Importing and cleaning data
- 5.6Managing metadata
- 5.7Joins and relationships
- 5.8Data blending
- 5.9Creating basic charts (bar charts, line charts, scatter plots)
- 5.10Building maps and geographic visualizations
- 5.11Using size and color in visualizations
- 5.12Dual-axis charts and combo charts
- 5.13Working with calculated fields
- 5.14Building hierarchies
- 5.15Creating sets and groups
- 5.16Trend lines and reference lines
- 5.17Advanced chart types (tree maps, heat maps, box plots)
- 5.18Dashboard design principles
- 5.19Storytelling with data
- 5.20Interactive dashboards and actions
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