GoLogica offers Data Science Course Training in Indianapolis, USA, designed to help you master data analysis, machine learning, and predictive modeling. Gain practical experience with cutting-edge tools like Python, R, and SQL. This course prepares you for a successful data science career with expert-led sessions and real-world projects.
GoLogica Academic's Master Program features a structured curriculum, paving the way to Global scope.
Industry Experience
GoLogica having a 15+ years of experience on career transforming programs with industrial oriented Skills.
Latest AI Trends
GoLogica Advanced Programs delivers cutting-edge AI Training, offering insights into the latest trends.
Hands-on Experience
GoLogica emphasizes practical learning with exercises, projects to equip you with real world application.
Learners Achievement
Maximum Salary Hike
150%
Average Salary Hike
75%
Haring Partners
2000+
Our Alumini
Data Scientist Program Details
GoLogica offers an excellent Data Science Training Program in Indianapolis, USA, meant to provide learners with the skills and techniques to go hand in hand with achieving excellence in the data science domain. The training program will provide participants with fundamental knowledge in coding, data manipulation, and more mainstream data science skills to empower them to handle complex data-related tasks by the time the course is completed.
During the course of the program, learners will learn key skills such as data acquisition, data scrubbing, data preprocessing, exploratory data analysis, etc. The course will entail the hacking tools of material, which are Python, R, and SQL like structured and unstructured data needed for big data management. Some statistical analysis in R and mathematical concepts will be introduced to provide a solid basis for exploratory data analysis.
With time, practical case studies will advance the knowledge base of learners on how to analyze complex datasets and convey findings into actionable business decisions. They are sure to provide a good foundation for competent and efficient handling of data science assets best for data-driven decisions.
Course modules:
Data Collection & Cleaning
Data Preprocessing
Exploratory Data Analysis (EDA)
Statistical Analysis & Mathematical Concepts
Python, R & SQL for Data Analysis
In the latter part of their course, participants will pair this up with three advanced levels, machine learning and deep learning. Participants will have strong theoretical sessions in learning and implementation on which learners will come to adequate informed conclusions based on predictive methods around supervised versus unsupervised methodologies. The program also delves into frontier fields such as deep learning and natural language processing (NLP). It will prepare learners for designing and optimizing predictive AI models. Practical experience through training using tools and libraries such as TensorFlow, PyTorch, NumPy, and Pandas gives practical confidence to learners in acquiring job-ready skillsets closely related to industry problems.
To address the issues of big data, the training program has incorporated a big data technology toolkit such as Hadoop and Spark to perform large data set analysis efficiently and communicate findings to the relevant stakeholders.
Another one of the most important key components for the course is data visualization, offering students the skills to create visualizations that are insightful using tools such as Tableau and Power BI. Team members gather to learn about building interactive dashboards and complete reports designed to better communicate their analytical results to clients and stakeholders. The latter skills blend technical knowledge with business decision-making, thus increasing the learners' worth to their organizations.
The program aims at providing practical training through real-life industry projects in finance, healthcare, and retail. These projects are designed to simulate real-life challenges, allowing learners to bring their knowledge to solving practical problems. Upon completion, learners will have built a professional portfolio showcasing their expertise in solving data problems faced in the industry, thus becoming competitive in the job market.
Apart from technical training, the course also offers career progression modules on resume writing, interview prep, and professional career guidance. This includes guidance on how to write an impressive resume, prepare for the technical interviews, and how to search for jobs. This holistic method ensures that the freshers are completely prepared to jumpstart successful data science careers.
The Advanced Data Science Training Program provides a competitive edge to the learner by allowing the combination of theoretical knowledge to be applied to real-life situations. GoLogica provides solid mentorship, flexible online learning schedules, and lifetime access to class materials for mastering data science in Indianapolis, USA.
After the course is finished, the participants will be able to manage large and complex data, find hidden patterns, and contribute to policy and framework decisions driven by data. Such participants have become a precious asset for a rapidly growing firm that is almost entirely run on ground-up information technology.
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Data Scientist Syllabus
Tableau
GoLogica Tableau Training helps you master data visualization and business intelligence with Tableau. Our comprehensive training program covers all the essential topics, including data connections, calculations, dashboards, and more. Enroll now and learn from industry experts!
WEEK 8-9 32 Hours LIVE CLASS
Why Tableau
Things you should know about Tableau
Product Line
Different level of Setting Terminology
Creating some powerful visualizations quickly
Connecting to Data and introduction to data source concept
Working with data files versus database server
Understanding the Tableau workspace
Dimensions and Measures
Tour of Shelves (How shelves and marks work)
Help Menu and Samples
Saving and sharing your work Building Basic Views
Creating Views
Marks
Size and Transparency
Highlighting
Date aggregations and date parts
Discrete versus Continuous
Dual Axis / Multiple Measures
Combo Charts with different mark types
Geographic Map Page Trails
Heat Map
Density Chart
Scatter Plots
Aggregation and Disaggregation data
Pie and Bar Charts
Analyzing
Sorting & Grouping
Aliases
Filtering and Quick Filters
Cross-Tabs (Pivot Tables)
Totals and Subtotals Drilling and Drill Through
Small Multiples
Percent of Total
Working with Statistics and Trend lines.
Basic Arithmetic Calculations
Date Math
Working with Totals
String Functions
Custom Aggregations
Logic Statements
Formatting your Visualization
Effective Titles and Captions
Introduction to Visual Best Practices
Working with Labels and Annotations
Multiple visualizations into a dashboard
An Introduction to Best Practices in Visualization
Making your worksheet interactive by using actions and filters.
Packaged Workbooks
Publish to Office
Reader
PDF
Tableau Server and Sharing over the Web
Scenario-based Review Exercises
Best Practices.
Data Types
Dimension vs Measures
Discrete vs Continuous
Application of Discrete and Continuous Fields
Rename Hide Unhide and Sort Columns
Default Properties of Fields
Create Aliases
Recap
Knowledge Check
AI with Deep learning
GoLogica AI and Deep learning is an industry designed certification. This is a specialization course which will help you to get a break into the AI and Deep Learning domain, with one of the most sought-after skills.
WEEK 10-11 40 Hours LIVE CLASS
FUNDAMENTALS PROBABILITY & ITS TYPES
PROBABILITY DISTRIBUTIONS
DATA SAMPLING & ANALYS
INTRODUCTION TO PYTHON
CONTROL STRUCTURE
STRING
LIST
TUPLES & DICTIONARIES
LAMDA
MAP
REDUCE
FILTER
MODULES & FUNCTIONS
PACKAGES & LIBRARIES IN PYTHON
FILES & EXCEPTION HANDLING
INTRODUCTION TO PANDAS
JUPYTER NOTEBOOK
NUMPY/SCIPY BASICS
MATPLOTLIB BASICS
INTRODUCTION TO SCIKIT
DATA LOAD & PREPROCESSING
INTRODUCTION TO ML TOOL
ML EXAMPLES & ASSIGNMENTS
Why do we need Machine Learning?
Which Industries can use Machine Learning Sample Examples
What is Machine Learning A General Introduction
Supervised Machine Learning
Unsupervised Machine Learning
Machine Learning Work Flow
Data Collection
Data Mining
Feature Engineering
Visualization
Algorithm Selection
Evaluation & Optimization and Prediction
Model Evaluation
Need of Feature Engineering
Feature Engineering techniques
Regression
Linear & Non-Linear
Examples & Project assignment Linear and Non-linear regression model building
Classification
Examples & Project assignment
Clustering
Project assignment Customer Segmentation
Machine Learning
GoLogica training on Machine Learning will provide all the necessary training to become a certified Data Scientist with Proficiency in Python. Learn different algorithms like supervised, unsupervised, and reinforcement algorithms. Get hands-on experience in statistical techniques, ML modules, ML algorithms, focus on deep learning, basics of regression algorithms, SVM and Improving Performance, etc.
WEEK 7-8 30 Hours LIVE CLASS
Data Science with Python
GoLogica provides Data Science with Python Training This course has been designed with a focus on quality and simplicity making it ideal for Beginners or for those looking for a refresher on Data Science with Python. It gives an engaging learning experience covering everything you need to know about Data Science.
WEEK 7-8 30 Hours LIVE CLASS
Overview
Life Cycle of Data Science
MapReduce Framework
Importance of Data Science
Tools in Data Science
Need of Python in Data Science
Introduction
Terminologies of statistics
Measures of Centers & Spread
Types of distributions
Overview
Data Types and Variables
Operators
if Statements and lops
Functions in R
Introduction
ML Fundamentals
Learning Technologies
Decision Tree
Random forest classifiers
Deep Learning
Time Series Analysis
Applied Data Science with Python
GoLogica provides online lessons to help you get better and advance in your work. Join our Python data science course to learn Python skills, work with data, and get better at stats and machine learning. Make cool pictures too Learn from teachers who know a lot, work on real projects, and grow in the world of data science. Come with us now for a practical learning session.
WEEK 7-8 30 Hours LIVE CLASS
Python Data Science-Centric Libraries
NumPy
NumPy Arrays
Select NumPy Operations
SciPy
pandas
Creating a pandas DataFrame
Fetching and Sorting Data
Scikit-learn
Matplotlib
Seaborn
Python Dev Tools and REPLs
IPython
Jupyter
Jupyter Operation Modes
Jupyter Common Commands
Anaconda
What is Data Science?
Data Science, Machine Learning, AI?
The Data-Related Roles
The Data Science Ecosystem
Tools of the Trade
Who is a Data Scientist?
Data Scientists at Work
Examples of Data Science Projects
An Example of a Data Product
Applied Data Science at Google
Data Science Gotchas
Typical Data Processing Pipeline
Data Discovery Phase
Data Harvesting Phase
Data Priming Phase
Exploratory Data Analysis
Model Planning Phase
Model Building Phase
Communicating the Results
Production Roll-out
Data Logistics and Data Governance
Data Processing Workflow Engines
Apache Airflow
Data Lineage and Provenance
Apache NiFi
Descriptive Statistics
Non-uniformity of a Probability Distribution
Using NumPy for Calculating Descriptive Statistics Measures
Finding Min and Max in NumPy
Using pandas for Calculating Descriptive Statistics Measures
Correlation
Regression and Correlation
Covariance
Getting Pairwise Correlation and Covariance Measures
Finding Min and Max in pandas DataFrame
Repairing and Normalizing Data
Dealing with the Missing Data
Sample Data Set
Getting Info on Null Data
Dropping a Column
Interpolating Missing Data in pandas
Replacing the Missing Values with the Mean Value
Scaling (Normalizing) the Data
Data Preprocessing with scikit-learn
Scaling with the scale() Function
The MinMaxScaler Object
Data Visualization
Data Visualization in Python
Matplotlib
Getting Started with matplotlib
The matplotlib.pyplot.plot() Function
The matplotlib.pyplot.bar() Function
The matplotlib.pyplot.pie () Function
Subplots
Using the matplotlib.gridspec.GridSpec Object
The matplotlib.pyplot.subplot() Function
Figures
Saving Figures to a File
Seaborn
Getting Started with seaborn
Histograms and KDE
Plotting Bivariate Distributions
Scatter plots in seaborn
Pair plots in seaborn
Heatmaps
ggplot
Types of Machine Learning
Terminology: Features and Observations
Representing Observations
Terminology: Labels
Terminology: Continuous and Categorical Features
Continuous Features
Categorical Features
Common Distance Metrics
The Euclidean Distance
What is a Model
Supervised vs Unsupervised Machine Learning
Supervised Machine Learning Algorithms
Unsupervised Machine Learning Algorithms
Choosing the Right Algorithm
The scikit-learn Package
scikit-learn Estimators, Models, and Predictors
Model Evaluation
The Error Rate
Confusion Matrix
The Binary Classification Confusion Matrix
Multi-class Classification Confusion Matrix Example
ROC Curve
Example of an ROC Curve
The AUC Metric
Feature Engineering
Scaling of the Features
Feature Blending (Creating Synthetic Features)
The 'One-Hot' Encoding Scheme
Example of 'One-Hot' Encoding Scheme
Bias-Variance (UnderfittingvsOverfitting) Trade-off
The Modeling Error Factors
One Way to Visualize Bias and Variance
UnderfittingvsOverfitting Visualization
Balancing Off the Bias-Variance Ratio
Regularization in scikit-learn
Regularization, Take Two
Dimensionality Reduction
PCA and isomap
The Advantages of Dimensionality Reduction
The LIBSVM format
Life-cycles of Machine Learning Development
Data Splitting into Training and Test Datasets
ML Model Tuning Visually
Data Splitting in scikit-learn
Cross-Validation Technique
Hands-on Exercise
Classification (Supervised ML) Examples
Classifying with k-Nearest Neighbors
k-Nearest Neighbors Algorithm
Hands-on Exercise
Regression Analysis
Regression vs Correlation
Regression vs Classification
Simple Linear Regression Model
Linear Regression Illustration
Least-Squares Method (LSM)
Gradient Descent Optimization
Multiple Regression Analysis
Evaluating Regression Model Accuracy
The R2 Model Score
The MSE Model Score
Logistic Regression (Logit)
Interpreting Logistic Regression Results
Decision Trees and Terminology
Properties of Decision Trees
Decision Tree Classification in the Context of Information Theory
The Simplified Decision Tree Algorithm
Using Decision Trees
Random Forests
Hands-On Exercise
Support Vector Machines (SVMs)
Naive Bayes Classifier (SL)
Naive Bayesian Probabilistic Model in a Nutshell
Bayes Formula
Classification of Documents with Naive Bayes
Unsupervised Learning Type: Clustering
Clustering Examples
k-Means Clustering (UL)
k-Means Clustering in a Nutshell
k-Means Characteristics
Global vs Local Minimum Explained
Hands-On Exercise
XGBoost
Gradient Boosting
Hands-On Exercise
A Better Algorithm or More Data?
What is Python?
Additional Documentation
Which version of Python am I running?
Python Dev Tools and REPLs
IPython
Jupyter
Jupyter Operation Modes
Jupyter Common Commands
Anaconda
Python Variables and Basic Syntax
Variable Scopes
PEP8
The Python Programs
Getting Help
Variable Types
Assigning Multiple Values to Multiple Variables
Null (None)
Strings
Finding Index of a Substring
String Splitting
Triple-Delimited String Literals
Raw String Literals
String Formatting and Interpolation
Boolean and Boolean Operators
Numbers
Looking Up the Runtime Type of a Variable
Divisions
Assignment-with-Operation
Comments:
Relational Operators
The if-elif-else Triad
An if-elif-else Example
Conditional Expressions (a.k.a. Ternary Operator)
The While-Break-Continue Triad
The for Loop
try-except-finally
Lists
Main List Methods
Dictionaries
Working with Dictionaries
Sets
Common Set Operations
Set Operations Examples
Finding Unique Elements in a List
Enumerate
Tuples
Unpacking Tuples
Functions
Dealing with Arbitrary Number of Parameters
Keyword Function Parameters
The range Object
Random Numbers
Python Modules
Importing Modules
Installing Modules
Listing Methods in a Module
Creating Your Own Modules
Creating a Runnable Application
List Comprehension
Zipping Lists
Working with Files
Reading and Writing Files
Reading Command-Line Parameters
Accessing Environment Variables
What is Functional Programming (FP)?
Terminology: Higher-Order Functions
Lambda Functions in Python
Example: Lambdas in the Sorted Function
Other Examples of Using Lambdas
Regular Expressions
Using Regular Expressions Examples
Python Data Science-Centric Libraries
Data Science Capstone
GoLogica Data Science Capstone is a complete training program that provides practical experience, mentorship, and industry-important skills to aspiring data scientists. Develop real-world projects, get practical experience, and release the power of data with this transformative program.
WEEK 15-16 60 Hours LIVE CLASS
About the Project
Dataset
Project
About the Project
Dataset
Project
To become a master in Data Scientist?
Skills Covered
Tools Covered
Career Support
Personalized Industry Session
This will help you to better understand the Data Scientist industry.
High-Performance Coaching
you will be able to grow your career by broadening your proficiency in Data Scientist.
Career Mentorship Sessions
With this, the students will be able to decide their careers in the right way.
Interview Preparation
We Help with face-to-face interaction through mock interviews & Exams
Program Fee
Program Fee: 98100 /-
88290 /-
Discount: 9810
Powered by
Paypal
Debit/Credit
UPI
Data Scientist Certification
GoLogica Data Scientist Certification holds accreditation from major global companies worldwide. Upon completion of both theoretical and practical sessions, we offer certification to both freshers and corporate trainees. Our certification on Data Scientist is recognized globally through GoLogica, significantly enhances the value of your resume, opening doors to prominent job positions within leading MNCs. Attainment of this certification is contingent upon the successful completion of our training program and practical projects.
Job Outlook
Career Opportunities & Annual Growth
The U.S. Bureau of Labor Statistics forecasts a 15% increase in employment for Data Scientist Expert from 2020 to 2030, significantly outpacing the average for all occupations. Additionally, Data Scientist Expert Ventures predicts 1.5 million unfilled Big Data Architect jobs worldwide by 2030.
Salary Trend
According to the BLS, Data Scientist Expert professionals are well-compensated. The median annual wage for Data Scientist Expert Specialist was $90,000 to $160,000 PA. It’s depending on factors such as experience, location, and specific job responsibilities.
Job Titles
Are you preparing for a interview? If yes, our expert tutors will help you with this.
Big Data Engineer
Data Engineer
Data Scientist
Machine Learning Engineer
Business Intelligence (BI) Developer
Data Architect
Analytics Manager
Quantitative Analyst
Data Scientist Faq’s
It is a field that makes use of computational and scientific methods to collect knowledge and insights from the data sets. Data science integrates mathematics, statistics, and computer science elements to accurately interpret them.
A data scientist is a professional who does programming, statistics, and machine learning to examine and interpret difficult datasets. They mainly focus on finding patterns, developing predictive models for predicting future behavior, and deriving insights to make business decisions.
It gives you an amazing career option with high demand and a high salary. It helps to solve real-world problems using analytical abilities.
Our training program is for both freshers and working professionals.
• Gathering, cleaning, and analyzing data.
• Building predictive models.
• Experimentation of new tools and techniques.
• Communicating insights.
• Staying updated with industry trends.
• Collaborating with other teams.
• Ensuring data privacy and security.
With this course, you will be able to differentiate yourself with multi-platform fluency. Also, will provide real-world experience with certain key platforms and tools.
No criteria. Knowing statistics, computer science, and Math’s would be good.
• Engineers.
• Data Analysts.
• IT Professionals.
• Freshers.
• Business Analysts.
• Python.
• Data Preparation.
• Data Analysis.
• Statistics.
• Querying Data.
• Clustering.
• Machine Learning.
• Text Processing.
And many more.
Yes.
24 weeks. However, it depends on convenience of the students.
No. We do not impose any specific order.
Our training program is a combination of self-paced and instructor-led. Also, helps the students to learn skills on their own and get guidance from industry experts.
Yes. The demand for data scientists is increasing constantly with competitive salary packages and it will continue in the future.
We will give access to the relevant courses after you join our training program. In fact, it is a part of our course.
• Data Scientist.
• Data Analyst.
• Machine Learning Engineer.
• Data Engineer.
• Business Intelligence Analyst.
• AI Engineer.
and many more.
Yes. We will issue the certificate after you complete the training successfully. It is a part of our training program.
Coding is a vital part of data science. Data scientists make use of codes to clean, prepare, & analyze data. They also use this to develop and deploy ML models. Also, will be able to automate tasks, develop custom tools, etc.
Yes. Our data science training program is for working professionals as well as freshers. It is suitable for freshers with basic knowledge of Statistics, Python, databases, and many more.
Both are equal and are in high demand when it comes to job opportunities. Data analysts focus on gathering, cleaning, and examining data to give business insights. But data scientists make use of their technical expertise for the development and deployment of ML models to solve complex business problems.
