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Data science

Introduction to Data Science

Data Science is that the study of Data. it’s regarding extracting, analyzing, visualizing, managing and storing Data to make insights. These insights help the companies to create powerful data-driven selections. Data Science needs the usage of each unstructured and structured data. it’s a multidisciplinary field that has its roots in statistics, science and technology. it’s one in every of the most extremely asked for jobs because of the abundance of Data science position and a moneymaking pay-scale. So, this was a short to Data science, currently let’s explore the pros and cons of Data science.

Advantages of Data Science

The various benefits of Data Science are as follows:

1. It’s in Demand

Data Science is greatly in demand. Prospective job seekers have numerous opportunities. it’s the fastest growing job on Linkedin and is predicted to make eleven.5 million jobs by 2026. This makes Data Science a extremely employable job sector.

2. Abundance of Positions

There are only a few people who have the desired skill-set to become a whole Data individual. This makes Data Science less saturated as compared with different IT sectors. Therefore, Data Science may be a vastly plentiful field and incorporates a lot of opportunities. the field of Data Science is high in demand however low in provide of Data Scientists.

3. A extremely Paid Career

Data Science is one in every of the most extremely paid jobs. according to Glassdoor, Data Scientists build a median of $116,100 each year. This makes Data Science a extremely moneymaking career choice.

4. Data Science is flexible

There are varied applications of Data Science. it’s wide utilized in health-care, banking, practice services, and e-commerce industries. Data Science may be a very versatile field. Therefore, you’ll have the opportunity to work in numerous fields.

5. Data Science Makes Data higher

Companies need proficient Data Scientists to method and analyze their data. They not only analyze the info however also improve its quality. Therefore, Data Science deals with enriching data and creating it higher for his or her company.

Explore the longer term of Data Science

6. Data Scientists are extremely Prestigious

Data Scientists enable corporations to create smarter business selections. corporations accept Data Scientists and use their experience to produce higher results to their clients. this offers Data Scientists an important position within the company.

7. No more Boring Tasks

Data Science has helped numerous industries to alter redundant tasks. corporations are using historical Data to coach machines so as to perform repetitive tasks. This has simplified the arduous jobs undertaken by humans before.

8. Data Science Makes products Smarter

Data Science involves the usage of Machine Learning that has enabled industries to make higher products tailored specifically for client experiences. for instance, Recommendation Systems employed by e-commerce websites offer personalised insights to users based on their historical purchases. This has enabled computers to understand human-behavior and take data-driven decisions.

9. Data Science will Save Lives

Healthcare sector has been greatly improved thanks to Data Science. With the arrival of machine learning, it’s been created easier to detect early-stage tumors. Also, several different health-care industries are using Data Science to assist their purchasers.

10. Data Science will cause you to an improved Person

Data Science won’t solely offer you a good career however will assist you in personal growth. you’ll be ready to have a problem-solving attitude. Since several Data Science roles bridge IT and Management, you’ll be ready to enjoy the best of each worlds.

Curriculum

Our corporate partners are deeply involved in curriculum design ensuring that it meets the current
industry requirements for data science professionals.

Foundations

Introduction to programming using Python
  • Syntax and Semantics of Python
  • programming
  • Conditional statements
  • Loops
  • Iterators
  • User-defined functions
  • NumPy
  • Pandas
Exploratory Data Analysis
  • Pandas
  • Summary statistics (mean, median, mode, variance, standard deviation)
  • Seaborne
  • matplotlib
Statistical Methods for Decision Making
  • Probability distribution
  • Normal distribution
  • Poisson’s distribution
  • Bayes’ theorem
  • Central limit theorem
  • Hypothesis testing
  • One Sample T-Test
  • Anova and Chi-Square
SQL Programming
  • Introduction to DBMS
  • ER diagram
  • Schema design
  • Key constraints and basics of
  • normalization
  • Joins
  • Subqueries involving joins and
  • aggregations
  • Sorting
  • Independent subqueries
  • Correlated subqueries
  • Analytic functions
  • Set operations
  • Grouping and filtering
  • Machine Learning Techniques
  • Linear and Logistic Regression
  • Multiple linear regression
  • Fitted regression lines
  • AIC, BIC, Model Fitting, Training and Test Data
  • Introduction to Logistic regression,
  • interpretation, odds ratio
  • Misclassification, Probability, AUC, R-Square
Supervised Learning Classification
  • CART
  • KNN (classifier, distance metrics, KNN regression)
  • Decision Trees (hyper parameter, depth, number of leaves)
  • Naive Bayes
  • Unsupervised Learning
  • Clustering – K-Means & Hierarchical
  • Distance methods – Euclidean, Manhattan, Cosine, Mahalanobis
  • Features of a Cluster – Labels, Centroids, Inertia
  • Eigen vectors and Eigenvalues
  • Principal component analysis
Ensemble Techniques
  • Bagging & Boosting
  • Random Forest
  • AdaBoost & Gradient boosting
  • Hackathon
  • Applications
  • Time Series (*Online Instruction)
  • Trend and seasonality
  • Decomposition
  • Smoothing (moving average)
  • SES, Holt & Holt-Winter Model
  • AR, Lag Series, ACF, PACF
  • ADF, Random walk and Auto Arima
  • Text Mining (*Online Instruction)
Data Visualization
  • Building interactive dashboards using Tableau
  • Data Visualization using Tableau
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