Palin Analytics : #1 Training institute for Data Science & Machine Learning
New Announcement! Upcoming batch of Azure Data Engineering from Tomorrow at 10:00 AM.
Data Science
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. It is used for examining, cleaning, manipulating, transforming and generating information from the data. Now a days in Business world data analytics plays a vital role to form decisions more scientifically and help to increase operational efficiency. We provide one of the best Data Science online Course, Training & Certification.
- English
- English, Hindi
₹75,000.00
₹39,500.00
Upcoming Batch Weekdays!!!
Starting from Wednesday
09:30 pm – 10:30 pm
You Save Rs. 35500/-
- 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
- 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
Top skill in demand now a days is to process raw data into business insights. There is no special programming language dedicated to data science but looking at the exciting features of the python language you can make your mind. Python has great features like fast and high computational capability, extremely compatible, cross platform support , distributed computing and vector arithmetic.
In this course we will learn python programming, statistics and analytics used for business 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, based on problems you will be able to select and apply machine learning models and deploy it.
Who can go for this
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 be a Data Scientist?
Are you interested in pursuing a career as a data scientist, it’s essential to create a roadmap that outlines the key steps and milestones along the way. Join us for an inspiring conversation where we will deep dive into your own journey and discuss the clear cut roadmap to become a data scientist. Let’s start the journey to be a data scientist in the exciting world of data science together!
Advantages
Countless Batch Access
Learn from anywhere
Industry Endorsed Curriculum
Industry Expret Trainers
Career Transition Guidance
Interview Preparation Techniques
Shareable Certificate
Real-Time Projects
Class recordings
Course Mentor
Tushar Anand
- 10 + Batches
- 4.8 Star Rating
- 859 Students Trained
- 450+ Successfully Placed
Hi I am Tushar and I am super excited that you are reading this.
Professionally, I am a data science management consultant with over 8+ years of experience in Banking, Capital Market, CCT, Media and other industry. I was trained by best analytics mentor at dunnhumby and now a days I leverage Data Science to drive business strategy, revamp customer experience and revolutionize existing operational processes.
From this course you will get to know how I combine my working knowledge, experience and qualification background in computer science to deliver training step by step.
Course Content
| LESSONS | LECTURES | DURATION |
|---|
| Probability | 1 Lecture | 20:00 |
| Random Variables | 1 Lecture | 25:00 |
| Probability Distribution | 1 Lecture | 21:00 |
| Central Limit Theorem | 1 Lecture | 25:00 |
| Sampling | 1 Lecture | 25:00 |
| Confidence Intervals | 1 Lecture | 25:00 |
| Hypothesis Testing | 1 Lecture | 25:00 |
| Chi Square Test | 1 Lecture | 25:00 |
| Anova Test | 1 Lecture | 25:00 |
Data Types | 1 Lecture | 50:00 |
| Basic statistics using data examples | 1 Lecture | 30:00 |
| Central tendencies | 1 Lecture | 43:00 |
| Correlation analysis | 1 Lecture | 34:00 |
| Data Summarization | 1 Lecture | 40:00 |
| Data Dictionary | 1 Lecture | 29:00 |
| Outliers /Missing Values | 1 Lecture | 30:00 |
| Basic Linear Algebra – dot product, matrix multiplication and transformations | 1 Lecture | 38:00 |
Overview | 1 Lecture | 12:00 |
| The Python Ecosystem | 1 Lecture | 15:00 |
| Why Python over R/SAS | 1 Lecture | 10:00 |
| What to expect after you learn Python | 1 Lecture | 35:00 |
Understanding and choosing between different Python versions | 1 Lecture | 34:00 |
| Setting up Python on any machine (Windows/Linux/Mac) | 1 Lecture | 24:00 |
Using Anaconda, the Python distribution | 1 Lecture | 20:00 |
Exploring the different third-party IDEs (PyCharm, Spyder, Jupyter, Sublime) | 1 Lecture | 30:00 |
Setting up a suitable Workspace | 1 Lecture | 8:00 |
Running the first Python program
| 1 Lecture | 23:00 |
Python Syntax | 1 Lecture | 15:00 |
Interactive Mode/ Script Mode Programming | 1 Lecture | 18:00 |
Identifiers and Keywords | 1 Lecture | 25:00 |
Single and Multi-line Comments | 1 Lecture | 28:00 |
Data Types in Python (Numbers, String, List, Tuple, Set, Dictionary) | 1 Lecture | 21:00 |
Implicit and Explicit Conversions | 1 Lecture | 22:00 |
Understanding Operators in Python | 1 Lecture | 26:00 |
Working with various Date and Time formats
| 1 Lecture | 28:00 |
Working with Numeric data types – int, long, float, complex
| 1 Lecture | 38:00 |
String Handling, Escape Characters, String Operations | 1 Lecture | 26:00 |
Working with Unicode Strings | 1 Lecture | 16:00 |
Local and Global Variables
| 1 Lecture | 12:00 |
Flow Control and Decision Making in Python | 1 Lecture | 15:00 |
Understanding if else conditional statements | 1 Lecture | 18:00 |
Nested Conditions | 1 Lecture | 25:00 |
Working in Iterations | 1 Lecture | 28:00 |
Understanding the for and while Loop | 1 Lecture | 21:00 |
Nested Loops | 1 Lecture | 22:00 |
Loop Control Statements– break, continue, pass | 1 Lecture | 26:00 |
Understanding Dictionary- The key value pairs | 1 Lecture | 28:00 |
List Comprehensions and Dictionary Comprehensions | 1 Lecture | 38:00 |
Functions, Arguments, Return Statements | 1 Lecture | 26:00 |
Packages, Libraries and Modules | 1 Lecture | 16:00 |
Error Handling in Python | 1 Lecture | 12:00 |
Reading data from files (TXT, CSV, Excel, JSON, KML etc.) | 1 Lecture | 15:00 |
Writing data to desired file format | 1 Lecture | 18:00 |
Creating Connections to Databases | 1 Lecture | 25:00 |
Working in Iterations | 1 Lecture | 28:00 |
Importing/Exporting data from/to NoSQL databases (MongoDB) | 1 Lecture | 21:00 |
Importing/Exporting data from/to RDBMS (PostgreSQL) | 1 Lecture | 22:00 |
Getting data from Websites | 1 Lecture | 26:00 |
Manipulating Configuration files | 1 Lecture | 28:00 |
Introduction to Data Wrangling Techniques | 1 Lecture | 15:00 |
Why is transformation so important | 1 Lecture | 18:00 |
Understanding Database architecture – (RDBMS, NoSQL Databases) | 1 Lecture | 25:00 |
Understanding the strength/limitations of each complex data containers | 1 Lecture | 28:00 |
Understanding Sorting, Filtering, Redundancy, Cardinality, Sampling, Aggregations | 1 Lecture | 21:00 |
Converting from one Data Type to another | 1 Lecture | 22:00 |
Introduction to Numpy and its superior capabilities | 1 Lecture | 15:00 |
Understanding differences between Lists and Arrays | 1 Lecture | 18:00 |
Understanding Vectors and Matrices, Dot Products and Matrix Products | 1 Lecture | 25:00 |
Universal Array Functions | 1 Lecture | 28:00 |
Understanding Pandas and its architecture | 1 Lecture | 21:00 |
Getting to know Series and DataFrames, Columns and Indexes | 1 Lecture | 22:00 |
Getting Summary Statistics of the Data | 1 Lecture | 26:00 |
Data Alignment, Ranking & Sorting | 1 Lecture | 28:00 |
Combining/Splitting DataFrames, Reshaping, Grouping | 1 Lecture | 38:00 |
Identifying Outliers and performing Binning tasks | 1 Lecture | 26:00 |
Cross Tabulation, Permutations, the apply() function | 1 Lecture | 16:00 |
Introduction to Data Visualization | 1 Lecture | 12:00 |
Line Chart, Scatterplots, Box Plots, Violin Plots | 1 Lecture | 12:00 |
What is machine learning | 1 Lecture | 15:00 |
Different stages of ML project | 1 Lecture | 18:00 |
Supervised vs Unsupervised ML | 1 Lecture | 25:00 |
Algorithms in Supervised and Unsupervised learning | 1 Lecture | 28:00 |
Introduction to Sklearn | 1 Lecture | 21:00 |
Data preprocessing | 1 Lecture | 22:00 |
Scaling techniques | 1 Lecture | 26:00 |
Training /testing / validation datasets | 1 Lecture | 28:00 |
Feature Engineering | 1 Lecture | 38:00 |
How to deal with Categorical Variables – Dummy variables | 1 Lecture | 26:00 |
Categorical embedding | 1 Lecture | 16:00 |
Detailed explanation of Linear Regression – Linear regression assumption | 1 Lecture | 15:00 |
Cost function | 1 Lecture | 18:00 |
Gradient Descent | 1 Lecture | 25:00 |
Linear regression using sklearn | 1 Lecture | 28:00 |
Model accuracy metrics – RMSE , MSE, MAE | 1 Lecture | 21:00 |
R2 vs Adjusted R2 | 1 Lecture | 22:00 |
Detailed explanation of Logistics Regression | 1 Lecture | 15:00 |
Cost function | 1 Lecture | 18:00 |
Logistics equation | 1 Lecture | 25:00 |
Model accuracy metrics – Accuracy, ROC, Confusion Matrix, AUC | 1 Lecture | 28:00 |
What are decision trees? | 1 Lecture | 15:00 |
CART algorithms | 1 Lecture | 18:00 |
Shortcoming of decision trees | 1 Lecture | 25:00 |
Bagging and Boosting | 1 Lecture | 28:00 |
Random Forest | 1 Lecture | 21:00 |
Gradient Boosting | 1 Lecture | 22:00 |
Explanations using sklearn | 1 Lecture | 26:00 |
XGBoost | 1 Lecture | 28:00 |
k Means Clustering | 1 Lecture | 15:00 |
DBSCAN Clustering | 1 Lecture | 18:00 |
PCA | 1 Lecture | 25:00 |
Support Vector Machines | 1 Lecture | 28:00 |
Naive Bayes Classifier | 1 Lecture | 21:00 |
Feature selection techniques | 1 Lecture | 22:00 |
Overfit vs Underfit | 1 Lecture | 15:00 |
Bias Variance tradeoff | 1 Lecture | 18:00 |
Grid Search | 1 Lecture | 25:00 |
Random Search | 1 Lecture | 28:00 |
Feature Engg examples | 1 Lecture | 21:00 |
Ridge / Lasso Regression | 1 Lecture | 22:00 |
SkLearn Pipelines | 1 Lecture | 26:00 |
SkLearn Imputers | 1 Lecture | 28:00 |
| TOTAL | 28 LECTURES | 84:20:00 |
|---|
What Our Students Say About Us
Palin Analytics
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!
Student feedback
Deepika
Kushal is a good instructor for Data science. He cover all real world projects. He provided very good study materials and high support provided by him for interview prepration. Overall best online course for Data Science.
Deepak Jaiswal
This is a very good place to jump start on your focus area. I wanted to learn python with a focus on data science and i choose this online course. Kushal who is the faculty, is an accomplished and learned professional. He is a very good trainer. This is a very rare combination to find.
Instructor
Thank you Deepak…
Add Reviews about your experience with us.
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.