Data Science with Python Course by Analytixlabs: Fee, Duration, How to Apply

Quick Facts

particular	details
Collaborators IBM	Medium of instructions English	Mode of learning Self study, Virtual Classroom +1 more	Mode of Delivery Video and Text Based	Frequency of Classes Weekdays, Weekends	Learning efforts 8-10 Hours Per Week

Course overview

The data science with Python course is developed and offered by the online education provider platform Analytixlab in partnership with the International Business Machines Corporation(IBM) for dual certification for the candidates. This online program consists of 130 hours of course study and eight to ten hours of self-study required by the students every week.

The course curriculum is focussed on the methods of using the Python programming language for data analysis and enables the candidates to acquire the data science skills which can be referred to as descriptive and predictive analytics done with the help of data manipulation techniques and sources from statistical libraries.

The Python programming language drives the application of web analytics along with the techniques of machine learning and artificial intelligence in the field of data science and this significance of Python is studied by the candidates during the course.

The experimental projects and course exercises enhance the practical learning experience of the candidates in data science with Python course training.

Course and certificate fees

Fees information

₹ 20,000

The data science with Python course training is available online in three different versions with specific fee amounts. The learners are provided with a student loan to pay the course fee and are allowed to pay the fees in three installments.

Data science with Python course fee structure

Heads	Amount in INR
Weekday Bootcamp	Rs 25000 + taxes
Weekend Batches	Rs 25000 + taxes
Self-Study	Rs 20000 + taxes

certificate availability

Yes

certificate providing authority

Analytixlabs

Who it is for

Data analyst Data scientist

The data science with Python course is aimed at individuals who have a background qualification in fields like engineering, mathematics, statistics, finance, and business management. The course helps those who aspire for a career as data scientists and professionals who work with machine learning technologies.

Eligibility criteria

The data science with Python course requires the students to have preliminary knowledge of the fundamentals of data analytics, Microsoft Excel, SQL, and Tableau software.

Certificate qualifying details

The candidates after completion of the data science with Python course classes ought to submit the completed projects and assignments without plagiarism within 1 year of course commencement and will receive the certificate from Analytixlab.
The dual certification with IBM is issued to the students who have completed their projects and assignments and cleared the MCQ tests in the two attempts within six months.

What you will learn

Data science knowledge Knowledge of python Statistical skills Knowledge of data visualization

The data science with Python course syllabus is framed for the candidates to equip them with the knowledge of data science, Python and data visualization. The candidates will learn about the strategies of data handling, visualization, statistical analysis, and predictive modeling techniques. This data science with Python online training improves the knowledge of data analytics.

The syllabus

Building blocks

Introduction to basic statistics

Introduction to Statistics
Measures of central tendencies
Measures of variance
Measures of frequency
Measures of Rank
Basics of Probability, distributions
Conditional Probability (Bayes Theorem)

Introduction to analytics and data science

What is analytics & Data Science?
Business Analytics vs. Data Analytics vs. Data Science
Common Terms in Analytics
Analytics vs. Data warehousing, OLAP, MIS Reporting
Types of data (Structured vs. Unstructured vs. Semi Structured)
Relevance of Analytics in industry and need of the hour
Critical success drivers
Overview of analytics tools & their popularity
Analytics Methodology & problem solving framework
Stages of Analytics

Introduction to mathematical foundations

Introduction to Linear Algebra
- Matrices Operations
Introduction to Calculus
- Derivatives & Integration
- Maxima, minima
- Area under the curve
Theory of optimization

Python for data science

Visualizing geospatial data

Introduction to Folium
Maps with Markers
Choropleth Maps

Operations with NumPy(Numerical Python)

What is NumPy?
Overview of functions & methods in NumPy
Data structures in NumPy
Creating arrays and initializing
Reading arrays from files
Special initializing functions
Slicing and indexing
Reshaping arrays
Combining arrays
NumPy Maths

Python essentials

Overview of Python- Starting with Python
Why Python for data science?
Anaconda vs. python
Introduction to installation of Python
Introduction to Python IDE's(Jupyter,/Ipython)
Concept of Packages - Important packages
NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc
Installing & loading Packages & Name Spaces
Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
List and Dictionary Comprehensions
Variable & Value Labels – Date & Time Values
Basic Operations – Mathematical/string/date
Control flow & conditional statements
Debugging & Code profiling
Python Built-in Functions (Text, numeric, date, utility functions)
User defined functions – Lambda functions
Concept of apply functions
Python – Objects – OOPs concepts
How to create & call class and modules?

Overview of Pandas

What is pandas, its functions & methods
Pandas Data Structures (Series & Data Frames)
Creating Data Structures (Data import – reading into pandas)

Cleansing data with Python

Understand the data
Sub Setting / Filtering / Slicing Data
- Using [] brackets
- Using indexing or referring with column names/rows
- Using functions
- Dropping rows & columns
Mutation of table (Adding/deleting columns)
Binning data (Binning numerical variables in to categorical variables)
Renaming columns or rows
Sorting (by data/values, index) -By one column or multiple columns - Ascending or Descending
Type conversions
Setting index
Handling duplicates /missing/Outliers
Creating dummies from categorical data (using get_dummies())
Applying functions to all the variables in a data frame (broadcasting)
Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc.)

Data analysis with Python

Exploratory data analysis
Descriptive statistics, Frequency Tables and summarization
Uni-variate Analysis (Distribution of data & Graphical Analysis)
Bi-Variate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)

Data visualization with Python

Introduction to Data Visualization
Introduction to Matplotlib
Basic Plotting with Matplotlib
Line Plots

Visualization tools

Basic Visualization Tools
- Area Plots
- Histograms
- Bar Charts
- Pie Charts
- Box Plots
- Scatter Plots
- Bubble Plots
Advanced Visualization Tools
- Waffle Charts
- Word Clouds
- Seaborn and Regression Plots

Statistical methods and hypothesis testing

Descriptive vs. Inferential Statistics
What is probability distribution?
Important distributions (discrete & continuous distributions)
Deep dive of normal distributions and properties
Concept of sampling & types of sampling
Concept of standard error and central limit theorem
Concept of Hypothesis Testing
Statistical Methods - Z/t-tests (One sample, independent, paired), ANOVA, Correlation and Chi- square

Predictive modeling with Python

Introduction to predictive modeling

Concept of model in analytics and how it is used
Common terminology used in modeling process
Types of Business problems - Mapping of Algorithms
Different Phases of Predictive Modeling
Data Exploration for modeling
Exploring the data and identifying any problems with the data (Data Audit Report)
Identify missing/Outliers in the data
Visualize the data trends and patterns

Supervised learning: regression problems

Linear Regression
Non-linear Regression
K-Nearest Neighbor
Decision Trees
Ensemble Learning - Bagging, Random Forest, Adaboost, Gradient Boost, XGBoost
Support Vector Regressor

Supervised learning: classification problems

Logistic Regression
K-Nearest Neighbor
Naïve Bayes Classifier
Decision Trees
Ensemble Learning - Bagging, Random Forest, Adaboost, Gradient Boost, XGBoost
Support Vector Classifier

Placement Readiness Program

CV preparation and expert sessions
Profile creation on Kaggle and GitHub
Mock interviews
Personal interviews focusing on effective communication and behavioral fit
Technical interviews on applications of data science concepts and techniques
Placement days - Actual & Practice Runs
Technical tests - MCQs and Hiring Projects/ Case Studies
Case Study Presentations
Interviews with Industry Experts
General Analytics and Problem Solving
Profile optimization on job portals (like LinkedIn, Naukri, Indeed, IIMJobs, etc.)
Continual feedback sessions pre and post interviews

Admission details

The course admission for the data science with Python course training is done online through the Analytixlab website

Step 1: Go to the data science with Python course page on the official website using the following link, https://www.analytixlabs.co.in/data-science-using-python

Step 2: Choose among the preferred versions of the course study.

Step 3: Click on the respective ‘enroll now’ link present below them.

Step 4: Fill in the required information for the course registration.

Filling the form

The candidates who wish to apply for the data science with Python course classes will have to enter their name, phone number, email address, course name, and phone number for enrolling in the course.

How it helps

The data science with Python course certification qualifies the students with technical skills such as Data Visualization, techniques for Data Mining & Analysis, the process of Data Blending & Manipulation, Statistical Analysis & Modelling in data science, Predictive Modelling, Python programming for Data Analysis.

FAQs

Which online provider platform offers the data science with Python course?

Analytixlab has developed the online course on ‘data science with Python’ and provides for the candidates interested in data science.

What is the dual certification program?

Analytixlab offers a dual certification in partnership with IBM to the candidates who finish the submission of projects and assignments along with clearing the MCQ tests within six months.

Is there a facility provided for loan applications for the students?

Yes, The candidates can apply for a student loan.

Can I pay the data science with Python course fee in installments?

Yes, you can pay the course fee in three installments.

How many projects and classes do the data science with Python course training include?

The course curriculum consists of ten projects and assignments along with twenty classes.

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Data Science with Python Course

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₹ 20000