How to Become Data Scientist

Some freshers or non-technical individuals want to become data scientists because the 21st century is an AI world.

There are primarily three major career paths in this field: Data Analyst, AI/ML Engineer, and Data Scientist.

1. Data Analysis

• If you have a technical background, you have some advantages because AI/ML work involves mathematics, visualization, and some technical knowledge. However, even if you are not from a technical background or have strong math and statistics knowledge, you can still become a data scientist.
• The most important thing is "DATA" Technical knowledge is okay, but domain knowledge and data understanding work equally. Data science revolves around working with data. If you have clean data, you don’t need to spend time purifying it, checking for NaN or missing fields, or correcting wrong data. On the other hand, if you have leverage data, you need to check data types, clean it, fill in missing values, and perform many other tasks.

First Step to Becoming a Data Scientist

We need to learn the basics of Python programming, visualization tools like Tableau or Power BI, and, most importantly, statistics. Apart from this,

1.1. Data

Data is a collection of information, staticts and this can be in various forms such as numbers, text, sound, images, or any other format.

There are four types of data in the data science

• Nominal
• ordinal
• Interval
• ratio

2. Second Step - Machine Learning and Advanced Machine Learning

In machine learning, you will encounter more than 10 algorithms and various technical steps for data cleaning, preprocessing, and imputation.

machine learning part is very important because all algorithms and problem solutions apply to the data.

Some important steps of data processing and EDA part.

Introduction

Problem Introduction If I chose cancer data we need to introduce what is data.

Problem Statement

The first step in any data clearly defines the problem you are trying to solve.

Import Libraries

Import the necessary Python libraries

• Pandas: For data manipulation and analysis.
• NumPy: For numerical computations.
• Matplotlib/Seaborn: For data visualization.
• Scikit-learn: For machine learning algorithms and model evaluation.

Data Acquisition

Data acquisition involves gathering the data from various sources.

Example. Loading data from a CSV file

data = pd.read_csv("xyz.csv")

Data Pre-Profiling

• Data Overview
• Missing Values
• Duplicates
• Outliers

Data Preprocessing

• Handling Missing Values
• Removing Duplicates
• Outlier Treatment
• Feature Engineering

Data Post-Profiling

After preprocessing, it's important to re-arrange the data to ensure that all issues have been Resolved.

• X-Y Split

The next step is to split the data into features (X) and target (Y).

• X = data.drop('HeartDisease', axis=1)
• y = data['HeartDisease']

Train-Test Split

To evaluate the performance of a machine learning model, the data is split into training and testing sets.

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Continuous and Categorical Split

Identifying continuous and categorical columns.

continuous_cols = X.select_dtypes(include=["float64", "int64"]).columns  
categorical_cols = X.select_dtypes(include=["object", "category"]).columns  

Encoding

One-Hot Encoding for categorical columns.

preprocessor = ColumnTransformer(
    transformers=[
        ("num", StandardScaler(), continuous_cols),
        ("cat", OneHotEncoder(), categorical_cols),
    ]
)

X_train = preprocessor.fit_transform(X_train)  
X_test = preprocessor.transform(X_test)  

Scaling

Scaling is important for algorithms that are sensitive input features, such as K-Nearest Neighbors (KNN) and Support Vector Machines (SVM).

Scaling continuous features scaler = StandardScaler()

X_train[continuous_cols] = scaler.fit_transform(X_train[continuous_cols])  
X_test[continuous_cols] = scaler.transform(X_test[continuous_cols])  

Concatenating

After preprocessing, the continuous and categorical features are concatenated back together to form the final dataset ready for modeling.

Concatenating continuous and categorical features

X_train = np.concatenate([X_train[continuous_cols], X_train[categorical_cols]], axis=1)
X_test = np.concatenate([X_test[continuous_cols], X_test[categorical_cols]], axis=1)

EDA-2 Steps (Assumption Check Strategy)

Exploratory Data Analysis (EDA) is an iterative process. After preprocessing, it's important to revisit EDA to check assumptions and validate that the data is ready for modeling.

Example. Checking the correlation between features

corr_matrix = data.corr()  
sns.heatmap(corr_matrix, annot=True, cmap="coolwarm")  
plt.show()  

Applying Machine Learning Algorithms

With the data clean and preprocessed, various machine learning algorithms can be applied to build predictive models some also like

• K-Nearest Neighbors (KNN)
• Linear Regression
• Logistic Regression
• Random Forest
• Support Vector Machines (SVM)

Example. Applying a Random Forest Classifier

model = RandomForestClassifier(n_estimators=100, random_state=42)  

model.fit(X_train, y_train)  
y_pred = model.predict(X_test)  

Model Evaluation

Evaluating the model

accuracy = accuracy_score(y_test, y_pred)  
print(f"Accuracy: {accuracy}")  

conf_matrix = confusion_matrix(y_test, y_pred)  

sns.heatmap(conf_matrix, annot=True, fmt="d", cmap="Blues")  
plt.show()  

Conclusion

Now our models are done, if the model working fine it's okay but if the model works fine we will apply all algorithms or evaluation models again and again.

3. Knowledge of ML Algorithms - The Final Step to Becoming a Data Scientist or AI/ML Engineer

After mastering machine learning, you need to gain knowledge of neural networks and computer vision along with advanced Python.

If you work with neural networks or computer vision models, you will need a large amount of data for processing. A machine learning model typically works with lakhs (hundreds of thousands) of data fields, while neural networks require 10x more data than traditional ML models. These algorithms are implemented using advanced libraries like PySpark.

After this, you can become a data scientist. Additionally, some domain knowledge of cloud computing is essential for roles like Data Engineer. Data engineers work with large-scale data and use ETL processes, PySpark, and Hadoop for data processing.