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Data Analytics with Python Course

The Python programming language has become a significant performer in the realm of Data Science and Analytics. This course proposes Python’s most powerful tools and libraries for doing Data Science. They are identified in the area as “Python’s Data Science Stack”. EagletFly’s Data Analytics with Python Course Training program is one of the best course in Delhi NCR & Patel Nagar.

Be Data Analytics in Python

This is a pragmatic course where the viewer will learn through real-world cases of how to practice the most prevalent tools for doing Data Science and Analytics with Python. In this course, you will see how to interpret data in Python using multi-dimensional designs in numpy, manage DataFrames in pandas, apply SciPy library of mathematical routines, and implement machine learning using scikit-learn!


Data Analytics is one of the passionate professions of the time. Mastering Python is simple for any IT based scholar. Python is in inclination these days and its center support is huge. Once you are a Python specialist, you will be capable of solving any data analytics obstacle with ease. All you require is to get complete information about Python and read Python with complete commitment.

Data Analytics with Python Course in Delhi
Data analyst Jobs after Course from Delhi

Career Opportunities for Data Analytics Experts ( Using Python Programming)

Career Opportunities and Jobs after being Data Analyst in Python.

  • IT Systems Analyst
  • Healthcare Data Analyst
  • Operations Analyst
  • Data Analytics Consultant
  • Data Scientist
  • Data Analyst
  • Digital Marketing Analytics Expert
  • Logistic Analytics Expert

Learn Python for Data Analytics

Python is expanding interest in the IT sector and the top IT learners opt to read Python as their preference of language for learning data analytics. The applicants want to dive into the career of a data analytics must have awareness about some language. If we compare Python with other languages, it is much more exciting and easy to acquire as compared to other programming languages. Thus, it has become a popular language for data analytics. Python is simple to learn and use whether you are new to the language or you are an accomplished expert of information technology. Python encourages you to serve the firm as a great data analytics.

Data Analyst with Python Training in Delhi NCR

Data Analytics with Python Course Frequently Asked Question ​

The program is NOT going to be easy. It will require at least 8 ñ 10 hours of time commitment per week applying new concepts and executing industry relevant projects. You can turn yourself into a certified professional via a professional data scientist course in Delhi.

EagletFly offers a data analytics course in Delhi at most affordable prices.

Preparatory Data Analytics Career Support

a). Mentoring on how to make the best resume for a data professional, highlight technical and domain expertise.

b). Data Science Interview preparation and interview mentoring by industry experts.

Access to Data Analytics Opportunities

a). Profiles of students will be circulated in the network of companies that Mapping Minds has.

b). For learners with 5+ years of experience, it becomes more difficult to convert these opportunities than others. For them, it is more about how to inculcate data-driven leadership in the current job and plan a transition into Data analytics or Data Science in the medium to long run.

This Data Analytics With Python Training program in Delhi is designed for professionals with limited data analytics experience to build their understanding from the basics to the advance. The program includes the following:

a). Statistics and EDA

b). Predictive Analysis

c). Domain Elective

d). Introduction to Data Management

The developer’s issue is bug fixing releases of older versions, so the stability of existing releases gradually improves. Bug fix releases, indicated by a third component of the version number (e.g. 2.7, 3.2), are managed for stability; only fixes for known problems are included in a bug fix release, and it is guaranteed that interfaces will remain the same throughout a series of bug-fix releases.

The latest stable releases can always be found on the Python download page. There are two recommended production-ready versions at this point in time because at the moment there are two branches of stable releases: 2.x and 3.x. Python 3.x may be less useful than 2.x since currently there is more third-party software available for Python 2 than for Python 3. Python 2 code will generally not run unchanged in Python 3.

No, the program is designed to be completed in its entirety, and cannot be taken as standalone modules.

If you like finding meaningful insights from the data, if you get excited by the prospect of informing business decisions through analysis and have an analytical bend of mind, then this program is meant for you. As long as you are able to clear the selection test (or are exempt) and are excited about the transition to Data Science- this program is meant for you. And Eaglet Fly is one of the well-established data analytics institute in Delhi that provides best data scientist course in Delhi.

Absolutely! Data Science is becoming a necessity for all industries and is no more a choice. Hence there is a critical demand for quality data professionals and because the supply is constrained, this is one of the most lucrative career options across industries.

Yes we do provide backup classes

We support multiple payment options online /offline. Choose an option that suits you the most.

Data Analytics with Python Training course ( at Patel Nagar , Delhi Center) will be a combination of theoretical and practical sections on each topic. We also provide exposure to our live projects related to Data science and data analytics in python programming.

Rs. 18,500  includes GST – Duration 3 months.

4 months Including live Data Analytics  Project Training sessions.

Data Analytics with Python Training Content

• Brief History
• Why Python
• Where to use

Beginning python basics:
• The print statement
• Comments
• Python Data Structures & Data Types
• String Operations in Python
• Simple Input & Output
• Simple Output Formatting

• Indentation
• The If statement and its’ related statement
• An example with if and it’s related statement
• The while loop
• The for loop
• The range statement
• Break & Continue
• Assert
• Examples for looping

• Create your own functions
• Functions Parameters
• Variable Arguments
• Scope of a Function
• Function Documentation/Doc strings
• Lambda Functions & map
• An Exercise with functions
• Create a Module
• Standard Modules

• File Handling Modes
• Reading Files
• Writing & Appending to Files
• Handling File Exceptions
• The with statement

• New Style Classes
• Variable Type
• Static Variable in class
• Creating Classes
• Instance Methods
• Inheritance
• Polymorphism
• Encapsulation
• Scope and Visibility of Variables
• Exception Classes & Custom Exceptions

• List Comprehensions
• Nested List Comprehensions
• Dictionary Comprehensions
• Functions
• Default Parameters
• Variable Arguments
• Specialized Sorts
• Integrators
• Generators
• The Functions any and all
• The with Statement
• Data Compression
• Closer
• Decorator

• What is Numpy ?
• Creating arrays from python objects
• Printing arrays
• Universal functions
• Indexing, slicing and selection
• Fancy indexing
• Broadcasting arrays
• arrays from python functions
• Mathematics operations
• Indexing a 2D array
• Practicals
• Finding patterns

• Importing Data from various sources (Csv, txt, excel, access etc)
• Database Input (Connecting to database)
• Viewing Data objects – subsetting, methods
• Exporting Data to various formats
• Important python functions: Pandas

• Cleansing Data with Python
• Data Manipulation steps(Sorting, filtering, duplicates, merging,
appending, subsetting, derived variables, sampling, Data type
conversions, renaming, formatting etc)
• Data manipulation tools(Operators, Functions, Packages, control
structures, Loops, arrays etc)
• Python Built-in Functions (Text, numeric, date, utility functions)
• Stripping out extraneous information Normalizing data
• Formatting data
• Important Python modules for data manipulation (Pandas, Numpy,
math, string, datetime etc)

• Introduction exploratory data analysis
• Descriptive statistics, Frequency Tables and summarization
• Univariate Analysis (Distribution of data & Graphical Analysis)
• Bivariate Analysis(Cross Tabs, Distributions & Relationships,
Graphical Analysis)
• Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/
density etc)
• Important Packages for Exploratory Analysis(NumPy Arrays,
Matplotlib, seaborn, Pandas etc)

• Iris dataset
• Data-point, vector, observation Dataset
• Input variables/features/dimensions/independent variable
• Output Variable/Class Label/ Response Label/ dependent variable
• Scatter-plot: 2D, 3D.
• Pair plots.
• PDF, CDF, Univariate analysis.
• Histogram and PDF
• Univariate analysis using PDFs.
• Cumulative distribution function (CDF)
• Mean , Variance, Std-dev
• Median, Percentiles, Quantiles, IQR, MAD and Outliers.
• Box-plot with whiskers
• Violin plots.
• Summarizing plots.
• Univariate, Bivariate and Multivariate analysis.
• Multivariate probability density, contour plot.
• Exercise: Perform EDA on Iris dataset.

• Introduction to Probability and Stats
• Why learn it?
• P(X=x1) , Dice and coin example
• Random variables: discrete and continuous.
• Outliers (or) extreme points.
• Population & Sample.
• Gaussian/Normal Distribution
• Examples: Heights and weights.
• Why learn about distributions.
• Mu, sigma: Parameters
• Symmetric distribution, Skewness and Kurtosis
• Standard normal variate (z) and standardization.
• Kernel density estimation.
• Sampling distribution & Central Limit theorem.

• Q-Q Plot: Is a given random variable Gaussian distributed?

• Uniform Distribution and random number generators
• Discrete and Continuous Uniform distributions.
• How to randomly sample data points.
• Bernoulli and Binomial distribution
• Log-normal and power law distribution:
• Log-normal: CDF, PDF, Examples.
• Power-law & Pareto distributions: PDF, examples
• Converting power law distributions to normal: Box-Cox/Power

• Correlation
• Co-variance
• Pearson Correlation Coefficient
• Spearman Rank Correlation Coefficient
• Correlation vs Causation
• Confidence Intervals
• Confidence Interval vs Point estimate.
• Computing confidence-interval given a distribution.
• For mean of a random variable
• Known Standard-deviation: using CLT
• Unknown Standard-deviation: using t-distribution
• Confidence Interval using empirical bootstrap
• Hypothesis testing
• Hypothesis Testing methodology, Null-hypothesis, test-statistic, pvalue.
• Resampling and permutation test.
• K-S Test for similarity of two distributions.

Introduction to Predictive Modeling
• Introduction to Predictive Modeling
• Types of Business problems – Mapping of Techniques –
Regression vs. classification vs. segmentation vs. Forecasting
• Major Classes of Learning Algorithms -Supervised vs
Unsupervised Learning
• Different Phases of Predictive Modeling (Data Pre-processing,
Sampling, Model Building, Validation)
• Overfitting (Bias-Variance Trade off) & Performance Metrics
• Feature engineering & dimension reduction
• Concept of optimization & cost function
• Overview of gradient descent algorithm
• Overview of Cross validation(Bootstrapping, K-Fold validation
• Model performance metrics (R-square, Adjusted R-squre, RMSE,
MAPE, AUC, ROC curve, recall, precision, sensitivity,
specificity, confusion metrics)

• Need for structured exploratory data
• EDA framework for exploring the data and identifying any
problems with the data (Data Audit Report)
• Identify missing data
• Identify outliers data
• Visualize the data trends and patterns
• Consolidation/Aggregation – Outlier treatment – Flat Liners –
Missing values- Dummy creation – Variable Reduction
• Variable Reduction Techniques – Factor & PCA Analysis

• Introduction – Applications
• Assumptions of Linear Regression
• Building Linear Regression Model
• Understanding standard metrics (Variable significance, Rsquare/Adjusted R-square, Global hypothesis ,etc)
• Assess the overall effectiveness of the model
• Validation of Models (Re running Vs. Scoring)
• Standard Business Outputs (Decile Analysis, Error distribution
(histogram), Model equation, drivers etc.)
• Interpretation of Results – Business Validation – Implementation
on new data

• Introduction – Applications
• Linear Regression Vs. Logistic Regression Vs. Generalized Linear
• Building Logistic Regression Model (Binary Logistic Model)
• Validation of Logistic Regression Models (Re running Vs.
• Standard Business Outputs (Decile Analysis, ROC Curve,
Probability Cut-offs, Lift charts, Model equation, Drivers or
variable importance, etc)
• Interpretation of Results – Business Validation – Implementation
on new data

• Introduction – Applications
• Time Series Components( Trend, Seasonality, Cyclicity and
Level) and Decomposition Classification of Techniques(Pattern
based – Pattern less) Basic Techniques – Averages, Smoothening,
etc Advanced Techniques – AR Models, ARIMA, etc
Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc

• Decision Trees – Introduction – Applications
• Types of Decision Tree Algorithms
• Construction of Decision Trees through Simplified Examples;
• Choosing the “Best” attribute at each Non-Leaf node; Entropy;
• Information Gain, Gini Index, Chi Square, Regression Trees
• Generalizing Decision Trees;
• Information Content and Gain Ratio;
• Dealing with Numerical Variables;
• other Measures of Randomness
• Pruning a Decision Tree;
• Cost as a consideration; Unwrapping Trees as Rules
• Decision Trees – Validation
• Overfitting – Best Practices to avoid

• Motivation for Support Vector Machine & Applications
• Support Vector Regression
• Support vector classifier (Linear & Non-Linear)
• Mathematical Intuition (Kernel Methods Revisited, Quadratic
Optimization and Soft Constraints)
• Interpretation of Outputs and Fine tune the models with hyper
• Validating SVM models

• What is KNN & Applications?
• KNN for missing treatment
• KNN For solving regression problems
• KNN for solving classification problems
• Validating KNN model
• Model fine tuning with hyper parameters

• Concept of Ensembling
• Manual Ensembling Vs. Automated Ensembling
• Methods of Ensembling (Stacking, Mixture of Experts)
• Bagging (Logic, Practical Applications)
• Random forest (Logic, Practical Applications)
• Boosting (Logic, Practical Applications)
• Ada Boost
• Gradient Boosting Machines (GBM) XGBoost

Learn Data Analytics with Python - We Data Analytics Expert

This Data Science with Python program will build your mastery of data science and analytics procedures using Python. With this Python for Data Science Course, you’ll get the basic concepts of Python programming and achieve deep awareness in data analytics, machine learning, data visualization, web scraping, and common language processing. Python is a demanded skill for many data science jobs, so jump starts your profession with this interactive, hands-on course.